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1119PDkttK3T44Ycnf39t25Xn+h0sxO9uIYxl4Pv+t2jRwrzzzjvBUNMuQ1/wWXU0zK4t3XzzzcY1jK+Gx9YLBr5hpXfffXfbboM8vaARTYWyjbaBZbdAyfw1c++fLQgggAACCCCAAAIIIIAAAggggAACCCCAQIUX8D3sjwZDbIHT+Jx8CqgNGjQozUy91sKU7fHC8vrUXHzaty0pwLbDDjvYNqXk6YG/Kylw4Or1uXLlSqO5C21Jc24qeOdLCgxu2LAh7UeBLM016EoLFiwI5oR1bXcFWXznqV5u6j2oQHGmpF6pvpRIaITP1OS6tpp/1haIveeee1J3EFm78sornT1qI8W8ZWy9YqN1TzjhhKBHa3xO2miZXJc176x6+N17773Wqvk+Z71woN8fW9J10YsOmtM0Uzr66KOdRfS7s8cee1i3F+J3N9/GOvE//vjDuOZu1vYbb7zROQdsCOcKbofbFZSPJ/1t8fUI1vcw0/XV918vUth+4n9bC2EbP2fW3QKp/Zjd5diCAAIIIIAAAggggAACCCCAAAIIIIAAAggg4BBw9aZU8WjgND60pLYrWBBNL730kvnyyy+jWWb77bc3J554YpCnYXxdvSgVgLQNyaqKroCctkXbmHLg2Eq25xmrFvQQjOeF667gZbhdnwpOZUoKfixdutQoWDpv3rxgWE0FXH2BP9exfVa9e/c2zZo1y9ScYHudOnW85eLfhyVLlhgFDW1Jx4zvT+c7atQoW/Fg+N0zzzzTui2eaQvgxsv41jUc8OzZs8348ePN448/bl555RWj7+nGpssvvzzopane1mEqxDk/88wz4eHTPnv16mW6dOmSlm/LiAfQomUUeHMF433fx9L43S2EsSzU49gV+Nxpp52MgvSZkusFDdVTz9H69eun7ULDUduG01VBDbt81FFHpdWJZ6iMrQd5vFyhbOPtYN0tQODUbcMWBBBAAAEEEEAAAQQQQGj/hikAACAASURBVAABBBBAAAEEEEAgo4BvGM0tt9zSqEdlmLLpcWrrjdS3b9/kcKszZ84Mel6G+4x+qsdaPBgXbi+J4Etx91HcemHb458aPlbzDSpYF/589dVXaUHoeL34uitw6gsQX3LJJfHdONddgTBVUJA7HsTx9XS1tVUByniP5bAxCuL45i4Ny+nTtQ9t23HHHaNFvcuaT1Y/Cn5NmTLFvPXWW+btt98OehH6juHaqeoMHjzYPPHEE8kihThnX+BUgfRsk/4euJIvAFoSvz+57KMQxnLxtVG9t7MZEtrXY9Vl7Lu+55xzTlbHdV3XeH6hbOPtYN0tQODUbcMWBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgo4Ae1LuCQh06dDDRYGl0OdxxtMepAmcKOEWT5sc777zzklm+4IIrMKDKxa2XPHCGfdgCe2FdXyDSVy+sH36qR6OGRVWvXF9P0rC871NDb7Zs2dJaxBXA1FyWmtcz27Ro0SJnUfVki/ekzfUajRs3zrl/9cB19d5zVrJsaNq0qSXXn6XvrHphhj0x165da/QdmDx5spkwYYKZOnWqc9jo+J5fffXVlKx8n/P06dPTeoCHDdJ36OCDDw5XM36qR7Er+X4Pcv1e2I7h20f82Pk2Dtvr+zvRrVu3sJj3M5fz1I5+//33YB5g106zPa6rfjy/ULbxdrDuFmCOU7cNWxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYwCvof98UBmpsCprbeperRtu+22yXb4AgPx44WVvv/+e2fwR724okOhhnXin+rRuXz58nh2sF67dm2zyy67WLcp09XmrbbayrRu3dpZL9ygwHT//v3NIYccYkaOHLnRQVPtV+ccD1wqf+HChWbFihXhoVM+Dz300JT1TCvz5893FtGcsPGUy3dJJgpClnaytTPXY6qn5d/+9jdz/fXXBz2F5TtixAiz5557ZtzVqlWrzLp164JyhThnX29EDc/qmtfXdmL6brlSWfndLYRxaOL6O6HtnTp1Cot5P337sBm/8cYbziF2NbRydMQA74Gz2FhI2yyaR5H/CRA45auAAAIIIIAAAggggAACCCCAAAIIIIAAAghshEAuD+ptgVM9TFfS3Ja2+SovuuiilNb5jhfvORZW9NVRr8tatWqFRZ2fvn107NjRGoTUznyBSAXOMg2/+cMPP5jDDz/cDBkyxNm2+AbNCavhjRVsdaXiWKkHcbZJ84b6AqE9e/ZM2ZV6iL7//vspeeGKgnMahjmaPvvssxKZRzS6z/hy1apVjebeLOm0zTbbmJNPPjk43wceeCDj7tUrUKkQ56yhVV2pc+fOrk3WfA1bbEvqubrrrrvaNjlfOlDh0vjdLYSxzkXDb3/66adWg8aNG6cNa20rqB7erpc7NIS57ff39ddft+0qyNt7772d24qzoVC2xWlrZa7DUL2V+epz7ggggAACCCCAAAIIIIAAAggggAACCCCw0QK+gGK8h5MtcBoO1Xv//fenDfmrYUDbtWuXbOOyZcvM4sWLk+vRhXr16hnXsKq5tDG6z+iyr3dj/Dyj9XzHdgUvw/oKJmu4VwUcXEm9GXV87Uu9IxWMDXu/+uafdB3bF+xs0KCBqxlp+RriVe23JfXQ1Vyg0aRzXLNmTTQruazgZXy+1C+++CK5Pb7QqlWrwCOen+u6zjd6XAWxNeyuLek6KCCaa9Iw1Or19+KLL1qrap/qmayU73NWT1ffcMvh98za8Fimfm8//PDDWO7/X/W9eOD7/fH93kUPlMvvbr6Nw3bqpQG9bGBL2Z6nz0o9RzWvcDz5/rZoOO2STIWyLclzqAz7InBaGa4y54gAAggggAACCCCAAAIIIIAAAggggAACpSKg+U2//PJL674VxGvSpEnKNvV6iicFZ9577z0zaNCglE116tRJ62XpCi6p4jHHHJNSP7qiwJQrZTP8rIJHzz77rGsX5rjjjnNue+GFF5zbfPU0P6d6JdoCGwqkKeDWq1cv06ZNG6OekfGkoOXYsWPj2cG6evhp6Nh4Uq9PzZ/qSvXr13dtSslX26+77rqUvOjKtddemzbEq8/p2GOPjVYPlmfNmpWWF2bomtqGfQ63F/dT3i7T0047zTz++OPF3bWz3pFHHpnclu9zVqBT3wlX0lCu2SS9HHHJJZc4i9qub1g437+7+TYOz3PMmDHhYtqnzydauDj78J1vNoFx/f1Wr1VX0PeII45IvnzgO1Zp/c5GfVjOTiD9X+ns6lEKAQQQQAABBBBAAAEEEEAAAQQQQAABBBCo9AK+QKbtYb8twPfrr7+aM844wyjYFk033nhj2vCdvuCaKwipoYBnzpwZ3XXKcjZzWJ511lnBUJopFf+3omE048PIhuUUMHINddqwYcOgp2hYNv6poXmnTJkSzw7Wb7nlFnPhhRdat4WZmk9z9erV4WrKp+ZKrVmzZkqeVmbPnu0MhGv75MmTzb777ptWL57Rr18/89Zbb8Wzg3X1DLa13Rf0sV3b+PclejBbz+bo9uIu24LY4b4+/vjjcDGnT/0O+X6PrrjiiuT+8n3OvuOpUUuXLjXq3Zsp3XHHHWb06NHOYrbrq8KF+N31nXNpfa90rq6/bTrm0Ucf7bQLN6xfv968/PLL4Wrap8v4l19+SSsbZvgswjJ6CWLAgAHhasqn/s5Ej+vbX2napjSKlYwCBE4zElEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCwC/iGdbUNL2l7OD5jxgwTDtcbHkU9Ik8//fRwNfhUjybXHJgqYDue8r/66qu0IYCjO9bcgr509dVXO4OAquc6rrYpmOYKFmh4UldSLz8NXexK6t3oSwpo+XrIFmeYXh1Pc1T+3//9n+/QQY/MYcOGOcsooBv/Hsjoo48+stbRULW2+S9tw46GOxg/fnxwzW09nMMyCjT16dMnCBaHedFPza2rgH40qRe05qy1JfWm0++Dy9ZWR/NR/vOf/7RtCvK6deuWMlR1vs9Zc8v6kgK+Gk7bl4YOHWpuuOEGZ5FGjRoZ1xDQhfjdzbexYNQ7XMOQ25J6lGsY6Exp7ty5zmGk9ZJE69atrbvwzbE8depU7zzJCxYsSBspIHqQ+PzUhbCNtofl7AQ2ya4YpRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTiAgp6upItoBgPmKluPGiqPPXwjPeI/Pzzz43mmLQlDSmpoJYtLVmyxJadzLvtttuSy9EFBS/PPfdcc/vtt0ez05Z9gTJfYNkWDAx3/v333wcB33A9+qk5NxVYdqVvv/02GIbXNRen6rmCtr45ElVPAUn1HnSlBx54wNh6GoflNa9p3759w9Xkp4Z8tn0PVGCvvfYyVapUSZYNF7bffvtwMe1TQVhf4FlBeH3Hhg8fHgRO1dM2+qP5OKM95cID7LjjjuGi9fOggw7KarheBW3vu+8+06JFC/PJJ59Y96WAVtw63+es9vnSI488YqZNm+YsooCpeh/7hvv1/f4U4nc338bC8/3e2f6O2sB9+9DvkCtA6huOVz1Y9XfXlvQdvvjii83vv/9u2xzMtXzYYYelbCuEbUoDWMlKgB6nWTFRCAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSBX44osvzKpVq1Iz/7fmCmTaAqfxHahM//7949nFDi74gow6iIIDvXv3NnrI365dO6OeW+pp9c477ziDWtHG+QIbvmCGei5qmOItttgiurtg2Td8puYUVLBqn332Sas3YcKEwM43pKyCkK7hiX3tDQ921VVXGc35qjlSFfRSgFYBYg216xsqVD1H1RPVFgT1Hdflm2luWg0hqnT++ecn51hUwPTdd98111xzTXCNw3OKf2qYaNv3Rj1AfcPqhsNOa67T3XbbLQiMKkCunnbq2fzdd98FvaYVgJ4/f378sCnrCjq2b98+JS/f51y3bt2gt6/r+6Sewl26dAmM9fujnqNz5swJhpiWczbDF7uur07cdg2iIKXxu5tvY52P7wULn0/Uoji/Q6qvYcZdwXv9buu63n333UYum2++uVm5cqVR7+orr7zS6IUHW9J1U4/3+O96IWxt7SMvs0CChAACCCCAAAIIIIAAAggggAACCCCAAAIIlGWBvn37JowxyR+tFzo99dRTyfZE26blXr16WZt38sknO+uE+zjuuOOsdfv37++se++991rrKHPx4sXOeuExi/u56aabJn7++WfnsTt37uw9dpUqVRK1atVK1KtXL/H0008n9/PJJ59469WoUSMwfuGFFxIjR45M3HTTTYndd9/dWyc8x2bNmiWPE11Ys2ZNYpNNNslqH+G+cvmMnl/0uFr2fS9efPHFePHkevv27TO2t1q1aokWLVokdtttt8QWW2yRsfyFF16Y3H98QUY1a9bMuI9cXGxle/bsmdiwYUP88MF6vs/5mmuuKdXznThxovU8lVmo3918Gx944IFO49mzZzt9oht8v//PP/98tGjKsv5+2L6D8Tz9rdpqq62yKqu/S66Ub1tXO0oyvyz++1zc89N1Z6jezIFlSiCAAAIIIIAAAggggAACCCCAAAIIIIAAAmkCxenhlE2PUw3/aEu+4/mG+9xuu+2Mes4VN7Vt29a45srMNP+gemf5knpArlmzxmho3mgvULXXN7+keqSOGDEiGE72pJNOMtddd5358MMPk4dyDcupAmqzLWn+WNeQqvFhk231fXmnnnqq6dWrl7OI79r6etwNHjzYOQRpeDANJarhRjV8r3qE+pJ6Ht97773OIltttVXQ+85ZoAQ2/P3vfzdPPfVUWm+9cNf5PudLLrnE6LyLm/S74/q9V76GkXWlQv3u5tNYv3OuuZs1t6nr9zVqpu+1evq6ku/v4/HHH28aNmzoqprM19+qn376KbnuWrj11lu9w3Xn09bVRvL9AgRO/T5sRQABBBBAAAEEEEAAAQQQQAABBBBAAAEErALFCXa5AijhAfbcc0+z3377havJTwW/ooHB5AZjTNWqVVOCjtFtWtb2Cy64IJ6d1bqCmZqL8s8//7SW9wUkVEGBn2yShrZs1apVsqjqnXnmmcn1XBY6deoUDInrquMa/tR3PQcOHGjq16/v2qU3/7zzzjOPPfaYs4yCywsWLLBub9SokfHNi6jvyj//+U9r3VwyNaTo1VdfbZ544glnwDLc39lnn22dpzXcXtxPfU9vueWWoA2+oHm+z1lB/Lvuuqu4pxXMEaz5MG2pdevWRsFBVyrU724+jefNm2d+/vlnK0GHDh2cQedoBQ2d6/obpd8f39y8Cmz7XhaIHse3rP1ozlv9HvlSPm197WCbW4DAqduGLQgggAACCCCAAAIIIIAAAggggAACCCCAgFVAgUzX/HYKdmjePFvKFDi96KKLbNXM7NmzjY5pS5pLUnPv+ZJ6ze27776+IinbFEjr16+fmTx5cjCfZ8rGyIqvN6SKnXPOOZHS7sWOHTumBew0l2guPf0UbLvjjjuCNtepU8d5sDfeeCOYxzVewBc4PeSQQ8zDDz+cVRAn3K96qd5zzz3mgQce8NabMWNGWCXtM5OvKmg+0vvvv9/ZKzhtp7EMBZVee+01o55ymb6fYdUhQ4aYBx98MOP3Liyf6VPzxerFAM29mk3K9zkrWHzFFVdk07RkGX13hw8f7u0xmenFA+2sUL+7+TIu5Pym4cXq0aOHuemmm8LVnD81j6/+rmT7ske+bHM+ESoEAgRO+SIggAACCCCAAAIIIIAAAggggAACCCCAAAI5CmjY03Xr1llrtWvXzhlQcg15qx2pZ9SJJ55o3efGBhdq1aplxo8fHwRDfcFIBUyPOuoo89577wXBOAX/Niawd/LJJwdD6qpnnS/ZAkjqbfnxxx+brl27+qoGAcMjjzwyaOeVV14ZDF27zTbbOOssX77cvPrqq2nbXYHTbbfd1jRt2tR0797djB492rRs2TKtbjRDZn369DGfffaZcQ27HC2/sddW+1KQe+bMmcG1i+7bt7zzzjsHwU/1dj3ssMN8Ra3bzj33XPPJJ58E5+jqxWut+L9M9bTUsLwTJ040EyZMMJm+I/F95fuc77zzTvPMM88YX1BebdTv+DHHHBMEgjVEs+t7pbLZBMYL9bur9uXDeGN91E7fPmx/W1Qnnq699lrz5ptvmmbNmsU3Odc1xK965OvvlIL/uaR82ObSHsoWCVTRRKcam5mEAAIIIIAAAggggAACCCCAAAIIIIAAAgiUVQE9ZB46dGiyeX379jXq9UbKXWDt2rVBgODrr782y5YtC4bybd68udFPixYtNmo+VF9rvvnmG6NhOVevXh0Mq6kgbY0aNcwWW2xh1Gu2QYMGzuoKdGo4zrlz55qFCxcGwauddtrJKEitYHNxh9F1HtCzQc/Tx44dGwTGFi9ebL777rugPTvssIPRUMuHHnqoM3Du2W2JbZo/f74ZN26cmTRpUnB9NX9s9erVTb169YKhkxVI6tKli7cnZK6N0ZyzU6dODQJYCrR/++235scffzQ//PBDMpCtYHZopB7GChpu7NyxYTvzec6aT/Pll18OvgP6Tuv7rKGlFUTTcNOayzb6XT7iiCPM66+/HjY15VO9bNu3b5+S51sp1O+u2pRPY59BaW/TsMrTpk0LemHrb45+f/SjeZN1XfWja3b00UcHv+/6O7axqbzbVqR/n3U9CZxu7Dea+ggggAACCCCAAAIIIIAAAggggAACCCBQ6gIV6cFsqWNxAAQQKDMCClivWLEirT3qcavgcrbDI6ftgAwEyohARfr3WYFThuotI18smoEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIVR0C9o21BU51hhw4dCJpWnEvNmVQgAQKnFehicioIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQNgR8c29mM79p2TgLWoFA5RIgcFq5rjdniwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAnkQ8AVONdcsCQEEyp4AgdOyd01oEQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJRzAV/glB6n5fzi0vwKK0DgtMJeWk4MAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECiHw559/mlmzZlkPvd1225nGjRtbt5GJAAKFFSBwWlh/jo4AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIVTGDOnDnm119/tZ4VvU2tLGQiUCYECJyWictAIxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCiCDBMb0W5kpxHZRPYrDye8Pjx483YsWPNunXrymPzaTMCCCCAgEOgevXq5rDDDjNdu3Z1lCAbAQQQQAABBBBAAAEEEEAAgf8vMGnSJNOvXz84EEAAgTIp8PbbbzvbpaAqf7+cPGwoZwL697gipSrGmEQikSg356Sg6SGHHFJu2ktDEUAAAQRyF3jzzTcJnubORg0EEEAAAQQQQAABBBBAoEILHH300eaVV16p0OfIySGAAAIIIFDeBY466ijz8ssvl8vTqFKliil3Q/WqpykJAQQQQKBiC/C3vmJfX84OAQQQQAABBBBAAAEEECiOwJIlS4pTjToIIIAAAgggkEeB8v7vdbkLnDI8bx6/3RwKAQQQKJAAf+sLBM9hEUAAAQQQQAABBBBAAIEyLNCwYcMy3DqahgACCCCAAAISKO//XpfLOU6jX722bdua/fffP5rFMgIIIIBAORPQOPhz5swpZ62muQgggAACCCCAAAIIIIAAAvkUaNSoUcrh6tSpY3bYYYeUPFYQQAABBBBAIL8CS5cuNatWrUoeNP7vdXJDOVko94FTBU2HDBlSTrhpJgIIIICATaBfv34ETm0w5CGAAAIIIIAAAggggAACCDgFTjrpJJ4LOnXYgAACCCCAQH4E9Gx36NCh+TlYHo5S7obqzYMJh0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUomQOC0kl1wThcBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNIFCJymm5CDAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKVTIDAaSW74JwuAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgikCxA4TTchBwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEKpnAZpXsfDndYgj89ttvZsqUKeaTTz4xy5cvN99//7355ZdfTL169cx2220X/DRt2tR07tzZbLYZX6liEJerKu+//76zvXvssYfZdNNNndvZgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiUVQGiXGX1yhS4XevWrTPDhw83zz33XBA0VfA0U6pbt67p3r27OeGEE8yhhx6aqTjby6HA4sWLTceOHa0t32abbcyqVaus28hEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMq6AEP1lvUrlOf2qSfpwIEDzc4772zOOecc89Zbb5lsgqZq5sqVK82wYcNMt27dzDHHHGNWrFiR59ZzuNIW+M9//uM8hAKqVapUcW5nAwIIIIAAAggggAACCCCAAAIIIIAAAuVBYPr06aZZs2YpP7Nnzy4PTaeNCCCAAAIbKUCP040ErEjVNQRr7969zeeff77Rp/XSSy+Z//73v+bZZ581++2330bvr6Lu4IorrjATJ060np56/LZs2dK6rVCZuqau9Ne//tW1iXwEEEAAAQQQQAABBBBAAAEEEEAAAQTKjYCmLVu4cGGyvVWrVjW77rprcp0FBBBAAIGKK0DgtOJe26zPLJFImNtvv91cf/315s8//8y6XqaCy5YtM8cff7z56KOPTIMGDTIVr5TbR40aZb766qu0c9fNWJMmTdLyC53h63H6l7/8pdDN4/gIIIAAAggggAACCCCAAAIIIIAAAgikCDz++OMp00vttttupkuXLill4ivxZ2Cqs/nmm8eLJdeLc4xkZRYQQAABBMqUAIHTMnU5CtOY/v37m6FDh5bKwb///ntzxhlnmNdff51hXGPC3333nTVoqmLt27f33ozFdpWX1fXr15uZM2c6j0WPUycNGxBAAAEEEEAAAQQQQAABBBBAAAEECiDw+++/m/POO8+sW7cuefS77ror58Dp3nvvnawfXyjuMeL7YR0BBBBAoGwIMMdp2bgOBWtFLkHT2rVrm3bt2pndd9/dbLHFFlm3edy4ceb555/PunxlKegb9rYs9t785JNPzNq1a62Xp3HjxqZ+/frWbWQigAACCCCAAAIIIIAAAggggAACCCBQCIEPP/wwJWiqNmR6+X/p0qVm8eLFKc311SnOMVJ2zgoCCCCAQJkSoMdpmboc+W3MZZddZoYMGeI9qAKk/fr1M7169TJ77LFHsuyGDRvMggULzLRp08zFF19sVq9endxmW9CQtD179rRtqrR58SE/ohC+m7FouXwu77TTTmb69OnWQ9aqVcuaTyYCCCCAAAIIIIAAAggggAACCCCAAAKFEoh3XNhss81Mhw4dvM3Zdtttzddff51SxtdhoDjHSNk5KwgggAACZUqAwGmZuhz5a4yGztWwFL50yCGHmIcfftioN2E8bbLJJqZ58+bBz7777mu6d+9u1CPRlcaOHWt+++23Mjf8rKu9+ciP31RFj1kWA6fbbLNNxjfyoufAMgIIIIAAAggggAACCCCAAAIIIIAAAoUUiHdc0FylmUbSq1atmmnUqFHWzS7OMbLeOQURQAABBPIuQOA07+SFP6B6h5511lnehmheUgVN9RZWptSsWTPz1ltvBQFWjelvSz///LOZOHGi6datm22zN2/RokVB71bNl7pixQqj3q716tULhoZt0aKF2XHHHb31S3qj2qA2KZDYpEkTs+mmm+Z8iEQiYWbMmGGtp/3qvDYmKUi9cuXKYOJ72eutuIYNG5abwHVJGPv85KMhV5YvX25++OEHU7duXbP99tsHP7o5zkfSd0BDv+gNRl1vtYGEAAIIIIAAAggggAACCCCAAAIIIFByAvGgZmlMj5WPY5ScCHtCAAEEEMgkkDkqlmkPbC93Ahp6d9myZc52q6fpsGHDnNttGxo0aGB69OhhRowYYdsc5ClAlW2aPHmyeeaZZ8z48ePNF1984a2mIYTV4/Wiiy4KgpnewpGNq1atMjfddFMkp2ixc+fOwfkoR4HaV155xQwaNMi8//775qeffkoWVJBt5513Np06dTK33HJLEHhLbowtPPvss8k5FRTUdHkoKPzkk08maysw27t37+S6bUFtfPXVV80bb7xhZDdnzhyjwFw8KTinQPdJJ51kTj31VKOhR7JJCr5qSGZbqlq1qhk6dKipUqVK2uZ8G6c1IJIh88ceeywwmjJlStADOrI5WNS5KLgvb32nNt9883gR6/pnn31mBgwYYN12+umnG32flHRNXnzxRTNw4EDzwQcfmF9//TXInzRpktlvv/2s9clEAAEEEEAAAQQQQAABBBBAAAEEEMhdQM+l5s+fn1KxpEd5y8cxUk6AFQQQQACBUhcgcFrqxGXrAOrlqICkK9WsWTPoaera7stX8NAXONXwvpnSe++9Z6677jrz9ttvZyqa3K4AlH4eeughc//995vjjjsuuc23oPk6FQy1pV122SXIfuedd8y5556bdpMV1lEP208//TT4ef75583NN99s+vfvb+Lnqt6yClZmk3RDd9pppyWLtmrVyhk4VSBOwcA777zTfP7558k6rgUFD/WjYYL/7//+LwgO3n333Rl77Spg/Oijj1p3q3khbEFTFc6nsbVxxgTne8cdd5gHH3zQKADsS3/88UcQJFegXO66pq1bt/ZVCbYpwO/yCXt3z54925xyyilBUDu6QwXGM82tES3PMgIIIIAAAggggAACCCCAAAIIIFDaAno+pec6CxYsSP6EIQa2kwAAIABJREFUo8Bp1K6DDjooePm8du3aG90UPa/RaHZ6XvXdd98FI4TVqlUrGOlNz2WOOeYYs+WWW2Z1nIULF5r169cHZadOnZpWR50IosFUjWQXHbpXz9r0fCia9KJ99NnXxh4juu/4cklahPvWi/vffPNNuBp8qiNIdBS9H3/8MXjZX50D1IlF56vnoy1btjQ9e/b0di5QnZdfftl8+eWXZsmSJUYdTXbYYQejYZFPOOEEs9VWW6UcmxUEEECgrAsQOC3rV6iE26eebr70r3/9yzqnqa9OuE3/kPpS9AYjXk43XgqY3nbbbdaekvHytnX1oj3++OODwHA2Qcr4MBrRfertswceeMBccMEFyZut6Hbb8po1a4Jer2vXrjVXXXVVShHXsLwphRwrrjfhdDwFWNWDsThJQd9Ro0YZ9b5UL1X1RHUl33ysviFO8mlsa7sC3+pZq5u2XNO8efOMzm306NHm0EMP9VZ3+ejGevfddw9u/hXQj/ZWDnfYpk2brG/+wzp8IoAAAggggAACCCCAAAIIIIAAAqUhoFHMNKqanhnpeZ0r/fvf/zZ67nHllVeaf/7zn6Z69equos78WbNmBaPBjR071rim/1JlBVE1OtiNN94YTN/l2qGeyWk6pDBwait31FFHpWR/9dVXKc9Chw8fbjR6WDTpmaNG21MqiWNE9x0ul7RFuF99PvfccynnVKNGDaPnikqaykqjqKljhqvDwfXXX2/uu+8+c+SRR0Z3a3Tdrr76avPhhx+m5EdXNEKgnjf37ds3ms0yAgggUKYFMncBLNPNp3G5COitHwWBXOmAAw4w5513nmtzxvyDDz44CHrqzSzbj95OsiXdcBx99NHm1ltvLXbQNLrfPn36eP/BDsu6gl260dMbdfoH3XejFe4n/nnDDTcYvaUVTb4AYrScbdkWmNSNzL777lvsoGn0OLr5002OL7msVMfWvnBfrnqlYRweM/zUSwJdunQpVtA03Ie+m/o+hTeTYX7803WeerPuo48+Cnr22oKm2o8rMB4/BusIIIAAAggggAACCCCAAAIIIIBAaQnoWdOJJ54Y9BLUdFO+oGnYBvXMVJBVz1/ivTTDMrbPFStWBD0R99prL/PSSy95g6aqr+cy6uCg6brUAcCVZs6cmdOzvPr166cETbXf+DOeRo0aJYOm2l4Sx4i2v7QsoseIn5NGPlNvUwVU27VrZwYPHuwMmmo/eqZ87LHHmmnTpgW7Xb16dTDi3+GHH57xGayerWnaOE3zRUIAAQTKiwCB0/JypUqgnffcc4/35kEBP1+v0BJognUXGsr0tddes24rTqaGn7jmmmsyVo3fNEQruObzjJZxLesNuTfffDNls+9YKQUtK7bApIK6egOwpJJ6rfpuiH2BX1v7wnb5zrukjcNj6lNDMF9++eXec4qW9y0vXbrUG1jWXLWuYZLr1KkTvI33yy+/OA/h83NWYgMCCCCAAAIIIIAAAggggAACCCBQQgJ6lqaemAqkqTNEPGkoW98zQwUzM72UH+5Tz4oUuFOPVtuxwnK2T40o9re//S2Yssu23fccyla+Y8eOadnxfcRfeI9vT9tBLMN2jLBIaVqEx9Bn/LmenkXddNNNQaA83vkjWi+6/OeffwY9i/WcbP/99zdjxoyJbs64rOfOvudjGXdAAQQQQCCPAgzVm0fsQh5KNyJ6W8yV6tWrZ/bbbz/X5lLL19tGI0eOdO5fN2XNmzcP3ihT771vv/02ePNLQ0CsW7fOWU9DRcydO9c5P6XG6tfk7bYU36/mUNhnn31M06ZNg7fK1INQNwu+9PXXX6ds1g2h6itpmytQrHk1DzzwwJS6Ou9o0pAhTz75ZDQrZVnzDxxxxBHBPARqu+ZX1dyxMnG9/adgr+Y62GmnnVL2pRX1SI3PgxAW0hwFarMt5ds4bIO+T76g7GabbRbMw9G2bVujNwv1PZkwYYLRjZ8rDRs2LBi2JDrnRVhWwzC7bvQ192mmROA0kxDbEUAAAQQQQAABBBBAAAEEEECgtAT0HExziE6cODF5CD1P0vRQGrJWQ99uvfXWRsFVzXeql8cVdJs9e3ayvBY0dK+m4NLwva40adIk07Vr17QepnpGozZouiNNaaThc/UsK/yJDuOrZ1tq2/vvvx/MpRk9lurffffdQZaeIWqI2GjSSHs6nzDtueee4WLw+dtvv6WdV/y5zcYeIzxgaVuEx9E56VlmNGk+Uj23C5NGtdPzSD2/VNKzrhEjRgTPMMMy+tSUWAokR58TykPz3eoa6nui+WMfeeSR4LsSravnk5oqLNN0WNE6LCOAAAKFFEiUp9S3b1+99pT80Tops8D777+fNIv6hctnnXVW5p2UcIkZM2YkqlWr5mzXjjvumBg/frz1qB988EGibt26zro6rxtvvNFaV5lPP/20t67qt2zZMjFt2rTEH3/8kbKfr776KlGnTh1v/ZtvvjmlTnRl0KBBzroDBgyIFk1bXr9+faJx48bO+tdff31i3bp1afWUMXLkSGc9ne/y5cut9V588UVnvYMOOshaR5mFMF62bFmidu3azvZ26tQp8dFHH6W1efXq1Yljjz3WWU8+r732Wlo9Zdxyyy3eeqob/2nUqFGiTZs2iWbNmiX+/PNP634rWyZ/2yvbFed8EUAAAQQQQAABBBBAAIHcBfi/Y+5mmWpcd911Kc8t6tWrl5g5c6a32m+//ZY4/fTTU+rp2ce4ceOc9ZYuXZpo0KBBSp0tt9wyMXDgwLRnb9GdDB8+PKVO+IxlzJgx0WJpy88++2xavSVLlqSVi2boOWC4//Bz4sSJ0SIpy8U5hnaQTwvbOYXn1rRp04TLcd68eYmtt946zSOsq+eTI0aMSGzYsCHFRCt6NnnAAQek1X3kkUfSypKBAAIVQ6Ai/fusv3MM1VvIkHUejz1u3Djv0Y477jjv9pLeqLHwNedp9I2x6DG6detmPv7442COhGh+uKy3mfSWk3oPutLUqVNdm9LmK4gXVC/Pd9991+y9995px2jcuHHGcfm32267+C6T674hPeLDfyQr/W9B13HRokXx7GBdc8xq2Itq1apZt+uNPlfafPPNjXod25KvvfG37qL1ffVUrjSMzz//fKPvli2pR7XentTcDfG0zTbbmKeeesrUrVs3vim5Hn3zMplpmfsiui1c1ht3d955Z9CzdeXKlcEbexpqWW/3aU4JEgIIIIAAAggggAACCCCAAAIIIJBvAY2opp6i0aSpvsKeh9H86HL16tWD3pzx53Ku0bx0nBNOOCEYSS7cj0Y907ORf/zjH2nP3sIy+jz11FPNhRdeGM0Kll955ZW0vGhGfHjahg0bmh122CFaJG05/ixLz2zivVKjlYpzjHxbxM8pbL+eeWoUNvX0taWWLVsGQ/natmm0uw8++MCcfPLJ1iGc9WzyqquuSquqZ2IkBBBAoDwIuKNO5aH1tDFrAQ3T6kq1atUyCrrlM+mmSMNu2NL2229vnn76aaNgli8pAKZ5AsKJyeNl40OGRLe7bhpURkN2KCir+SldKVOAUzcQrhS/qQrLZboZU7mHHnooLJ7yqRuSIUOGpOTFV3Qz5Equ4XZV3tVebStu4LQ0jDWMiOZqtSXdHI8ePdo7XEyNGjVMnz59giF5bftwzfng+y5pP61btw7apSGnSQgggAACJSOgodB1bxMfXr9k9s5eEEAAAQQKIaCH8IcddlgwhGMhjs8xEUAAgcoooOcomqIpTHo2pQBnNkkv4GsKpGiwVC+O29Lll19uNA9qmGrWrGkU+GzSpEmY5f286667zDPPPBNMRxUW1FC9vhR/XpPpWZ72Fa+jZzpqqyvFy2dzjHxb2J7r6dppjll1pPAl27NRTdv10ksvmdq1a/uqGpWLp0yB63h51hFAAIFCCRA4LZR8Ho+rseynT5/uPKImVXf1UnRW2ogNP/30k3deU42Db/uH2XZItd0VOP35559tVYJ5PvVWlCvdeOONGY8vU1/SfAy2pDeronMIRMtoLgAF73zJFRhUHc0H60qag6J///6uzcH8BLaNmrvTdyPquiHUfBP5NtYbka505ZVXOnvURuu4et2qjObyiCfNV6s5M1zpgAMOMK+++qr3JttVl3wEEEAAAbuAgqaHHHKIfSO5CCCAAALlWkD39G+++SbB03J9FWk8AgiUJ4GRI0emNHf9+vVmw4YNKXm+lUGDBpm1a9cmi6gXYzzpRfZ77703JVtBUI1Elm1Sz9Y99tgj+DcirLNmzZpwMe1TvTpnzpyZku96hhUtFA8y+joMFOcYhbCIB3c32WST4LnsjjvuGD1163I0qB4WGDBgQNBJIFx3fdo6IDRt2tRVnHwEEECgTAkQOC1Tl6N0GqOgmW58XElDY+Qz6Y0mWxBKbdAE4YcffnjWzfH17HQFNzUhumubLLJ5s84V/FTD9bac3rizJfWKdKVsbuB8wdFwv7rB1dt+uu7z5s0zmmxeAVeXueq5jv3ZZ58Z242O6ugtMfXktKV8G+t89b2yJQ2/e+aZZ9o2peUpUJxLit98RuvqWt13330ETaMoLCOAAAIlIOAbRaMEds8uEEAAAQQKLKC/875pRgrcPA6PAAIIVCgBBf/iSdNA3X777fFs63qPHj2s+dHMO+64I7pq9t9/f3PkkUem5GWzEg+6/fLLL85qGgI4/hzMFwTVjlatWpXW2cFXpzjHyLeF7Zw0lZU6omST5s+fn1JMHW9OPPHElDzXip5JxlOzZs3iWawjgAACZVKAwGmZvCwl26j4P3LxvTdo0CCeVarrw4cPd+4/m6BltPJ3330XXU1Zdg0P4gt2aWgovXmVKfl6U/puqnzH9tVztUdBTQVGNSxx+KMhkNXjM5fkCpwWt72+eqVhrOFdbDf7MtCcp765S6NOrn2ojO1NPN95du/ePae3J6PtYBkBBBBAwC3A8LxuG7YggAACFUGAv/MV4SpyDgggUF4EbCOfKbi3ePFic9lll5ndd999o07l008/TRvJrG/fvsXaZzwQ6nrup53He47qWd9ee+3lPa6ts4PvWV2uxyiEhe2cevbs6XUIN6pjhoLD0aRnepmmVgvLx5+ZqQNGvp9Bh23hEwEEEMhVgMBprmLlsHymwKnmFM1XUlBPgT5b0rAbrgnJbeWVZxsyIizreospfmMTltdnt27doqvO5fg//tGCriCkyviO7asX3b+WNUzhsGHDgjkF4jeO8bKZ1nWjqQnfbcnX3lxuHqP7Lg3jcePGRQ+RsqwbPVcP45SCGVbibzaquO97kOt3OcPh2YwAAggg4BDQUPd6a52EAAIIIFA+BfT/w/iD2fJ5JrQaAQQQKH8CnTt3DuYOjbf86aefNvrRsxBNk6EeigcddFBW0yBF9/Xkk09GV4N5L/UMytYbMaWgZSXeecI12puqxp/XZJqr1FZHQWX9X8OVcj1GISziz/UUQD7uuONcp5SSr1Ho4sMhZ9vbVDuKH9v3HDHlwKwggAACZUCAwGkZuAil3YRFixZ5D5HPt3100+UaDvXAAw/MOLdo/ESiE9DHtzVv3jyeFazHb2yihTp16hRddS779uG7EXDV04TpuonLlNQj8uKLLzZDhgzJVDTr7XrjzjUEsKu92rkv0OurV9LGMpkwYULW51vcgvG3LDX8tWv+V3lq2GkSAggggEDpCyhoWpL/LpZ+izkCAggggEBUoF+/fgROoyAsI4AAAnkUUO/PyZMnB3Ne2g775Zdfmoceeij4UdBNz7yOPvro4KdNmza2Ksk8Pf/Tc8Bo+umnnza6F2u4P9/0XfGgne8ZVri/eJ0OHToYdfJwpXh53zEKZRF/PqdAebYdaOLnJwcFz7NJy5cvN+o8E02+56XRciwjgAACZUHA/de/LLSONuRFoF69eht1HA1b+8MPP1j3se2225p27dolt7kCTSrQqlWrZLlsFhS4euedd5xFdSMXTxraVkNj2FLjxo2dc5NGyysQrRsAW9INlW6sbGnhwoVmxYoVtk1mzz33zDhEsIw1lLF6m2abdDN07LHHBvu+//77rdVcN3YaIkvD/9qSAoOuIU7ybaw34HTjXZqpatWqacPuzp0716xdu9Z6WL2RmM8XEqyNIBMBBBBAAAEEEEAAAQQQQAABBBDIIPDII48EAUJfZwftQiN6TZ8+Pfi5+uqrg+dfmg/1qKOOsh5BAdlMnTmsFbPMjL/gHlbTM6J4j9ZsgnbxIKOvTq7HKJRF/Jw01G62KV53p512yvpZl22IYJ9ntm2iHAIIIJAvAQKn+ZIu4HF8b0epWR9++GEQuCtOE/Xm2T777GNc89BoGIpo4HTBggXOw2T7xlO4g3fffdcZiNTws7bAqQK3rh6v2f4DHr9xCNujTwXMbPNDaJuvnit4Ge77m2++MV26dDEKErrSlltuGbz5p33p5lHB2PDtu969e7uqOXuO6nvhmitVw6rUqlXLus98G3/xxRfWdihTwfhMts7KkQ0KglavXj2Ss3HXM2VHrCCAAAIIIIAAAggggAACCCCAAAIFEtDzJD2/u/zyy83DDz9sxowZY3wjvIXNnDVrVvDs7Zxzzgl6pIb54efzzz8fLpbKZ/v27a371XMpBXmjKdOzIfWO/P7776NVgmdsKRmRlVyPUQgL2zllcoicYtpzzGyfm2of8d6q6oDRsWPH6O5ZRgABBMq0AIHTMn15SqZxm266qXdHmh/yzDPP9JZxbbzyyiudQVPdwPTq1SulanyYhujGXAOno0aNilZPWVbPzM033zwlTyu+4GW2NwDF3Uf8piHauEw3LnfeeaczaFq7dm1z9913B71RXUFbvRHoSq7z9rXXVacQxr7AqebhuPfee12nvlH5xf0ebNRBqYwAAggggAACCCCAAAIIIIAAAgiUgsBuu+1mNFrZ4MGDg8DXG2+8Yd56661g2fVivZqhYKtGO+vWrVtKqzRSVzTpBfxHH300mrVRy65nU/HnNZnmKlUj4nWU53tWFy+f6RiFsIg/19NQy+pkkU367bffzEcffZRS1OWdUuh/K3EfXwcMW33yEEAAgUILEDgt9BXIw/F32GEH71F0E6R5IjP1TI3v5MUXXzS+4OXtt9+eNvysay5N7Vv/gGebNOTtc8895yz+97//3bot/g93tJDvhihazrcP301Ecetp6NvHH3882oTkcsOGDc3bb79tWrRokcyLLyhoqmGCbck3zIavvT6r4taLts+3j7ixeuO6Ui7fKdc+XPnxG9BouXgbo9tYRgABBBBAAAEEEEAAAQQQQAABBMqqgJ7d7b333sHP9ddfb37++edg2qhXXnkl6I1qm65LQ/bGA6fxF93r169vevToUeqnHX9eo2Bhpk4l8Tqa1qxJkybOtsbLZzpGISziz9ZyCV7aRqHL5VlXfKjeXOo60dmAAAII5FEg+0hVHhvFoUpWQDc7vqQbHg3DkUuaM2eOOfXUU51VDjzwQGMbNz8+3Gl0B5MmTYquepfPO+88s2rVKmuZZs2aGU12bkvxG5uwjG6gXHOThmX0qXlVZ86cGc1KWXYFFPVmnoYwsSUFP/XjSpprQjeptnTFFVd4g6aq8+9//9tWNchztVcb4zdY0Z34bnjybVytWrVo01KWSytw+ssvvxj9DthSprcMbXXIQwABBBBAAAEEEEAAAQQQQAABBMqiQM2aNYMepcOGDTOLFy82F154YVoz4z1SNU2WykbTNttsE10tteX48yzfs6+wEfE6vudeqhMv7ztGoSzibcx0TqGFPuPP9vTcNNveqvPnz097ZpvLsaPtYBkBBBAolAA9Tgsln8fj6h+2qlWrOuerVFNOOeUUowBU9+7dvS3TP/YKxGneA1cwTzu44447rPvRcLzxOQPCguo5mU0aMWKEGT16tLOo3oazJfVMXLZsmW2TadOmjdGcDpmShtZYu3attZhuJFu3bm3d9vHHHxsNc2FLvjH+NSeDhklxpdNOO821KcjXnBTPPvuss4zrxm716tVGNzq2pCGQNXyLLRXC2DU8sdo3fvz4jL2pFQzv06ePmT17tu2UzEUXXWTOOOOMlG0KgqueLSkAn+lNRls98hBAAAEEECgPAro3Ka0Xk8rD+dNGBBBAAAEEEECgvAtoVLJoZ4TtttvONGrUKKvT0rMvTYk0depUo3k+wxR/DqJeq/FR5zTVVHGSRp2LTv2lY+2xxx7WXS1ZssToJ5oyBe00Cl+8s4OvTq7HKIRFrucU9dJyPOiq55269tmkeF3V8Xlms0/KIIAAAvkWIHCab/ECHE+Brr/97W9GcxO40u+//x4MlzF06FBz0kknma222iqlqAKmChpecMEF5p133knZFl+5+eabnfMAqPdrfIz8sP6XX35pLrnkEnPPPfeEWSmfClRprk8N/+FKZ511lrMnrO0f7nA/2f4D7tvHXnvt5XyQGH9TKzyuPnfdddfoasqygszRm8PoRvXe3XrrraNZKcvffvttcN1dgV4VdgVtfee5++67B4H4lIP9b8VXr7SMfXPj6rumwPPFF19sa67R91rfmeHDh1u316lTxxx33HFp20riPNN2SgYCCCCAAAJlREAPWiZOnGgWLFhgdH+mHz1g06dGKtFQ/5omQD/NmzcP7icyjXBSRk6NZiCAAAIIIIAAApVe4KqrrkqZ/krP4u6+++6sXRQIVKA1Gjjddttt0+qrg8avv/6azNe9ZK5J9Q8++OCUZ4l6TuPqUGF7XuPqNBC25ZNPPjEaWSyafHWKc4x8W2iUtPg5ZftcTg7xc9yYunp+2b59+ygvywgggECZFyBwWuYvUck08J///Kc3cKqj6CHZOeecYzQMbtu2bc0+++wTvB2m4JP+wV2zZk3Gxlx77bVGx3IlDeGrSeNdSW+tqbejesAqEKmJ49XzUW9+DRkyxLz33nuuqkYBPU1g70q+4GW2NwDxG4fosXz78NXTg0jdCG6xxRbR3QXL8ZucaIF169aZadOmBdcpmq/lCRMmmP79+5vPPvssvim5rhtdmdmSr72+m8dCGB966KG2U0jm6TupdP7555twqGgFTN99911zzTXXBG9JJgvHFm688UZrcLokzjN2KFYRQAABBBAouICGWHviiSfMbbfdFgRJXQ0Kg6nRl/KOPvpoM3DgwCCQ6qpHPgIIIIAAAggggEDhBTSCSDTlOpqI7hnjz41OP/306C6DZQVXP//882S+5vnUfWTTpk2TeZkW9Gwr2gFjs802M+FzHlvd+PMa9abVS3++FK+jsq6OBtoWL5/NMfJtEb8+vtHj4jbqjRyfk9X3zDNeP+6j3sEaCZGEAAIIlCcB5jgtT1drI9qqOT/V6zSbpBso3ZQ89NBD5sEHHwwCS9kETfXG2k033eQ9hN4Ka9CggbeMHth17drVqLefhoHQ5OW9evXyBk3V8/L55583uhFwpfhNQ7ScLxgYLefbh+8mIn7DEd3nqFGjgmGCdQ7169c3Goo4TNE388K86GeXLl1M7969gzlqNSSvevvqhkT5n376abRo2vLOO+8cBKbTNljeLIuW8Z2nz6e0jBs3bux9c01DSuvtSQXh1btXb7lpWOaDDjrIGzTVnB26Qbcl33n6fGz7Ig8BBBBAAIFCC2jkEd33qffo2Wef7Q2autr68ssvBy/eXXbZZebHH390FSMfAQQQQAABBBBAoMAC8cCp7xmHranPPPNMynC4es537LHHphXdf//90/JeeeWVtDxXhuZU1U80XXHFFc5OACo3Y8aMaHHnkL7RQvHz32WXXYJnktEy0eXiHCPfFvFz8o0eFz03LcfPT3nZPuvS/ys+/PDDlF1mWzelEisIIIBAgQUInBb4AuTz8OrpqbegSiPpIZl6J2RK6vF36aWXZioWbFevwEyBQxXU3AaPP/64adasmXO/uimMDiESLaggmuY4zZTUFvW8dSVfYHDlypWuakG+zlXBaQ3NG+0FWrdu3WDuWVdl9UhVoFUBaQ2xfN1116XcoPjeGvSdc/wGK3p81w1PIY3V09h3rmq/bt70pqNeCsj0vVIwWr2fbck3fHK9evVyenPStn/yKp+Aep3r7/MJJ5xgevbsWfkAOGMEECiogOZ/b9euXTDiyKJFizaqLfq39q677jIHHHCA0YtLJAQQQAABBBBAAIGyJ6BnTdGk0d3iAcro9uiygqYa0Sua+vXrZ+1ReOSRR0aLBcsKfGYKnuoZ2plnnhlMrRTdgUY40chgvhQfDlij62VK8Wdgrude4X6Kc4x8W+R6TuG56TPeY7RGjRrBC5LRMq5lPXPTCHnRlMkzWpZlBBBAoKwIEDgtK1ciD+1QYPH1119Pm790Yw6tQKjmHR0wYEDWu1Hg9JBDDsm6vK+g5lDQMHHHHHOMr5iZN2+e8wFehw4dguCrdwfGBMMFu264NM/mjjvu6NxFtgFr9Tpt1apVcj+qp5vF4qROnTp5b3xd86NqTtXly5dbD6lewHrzzpYKabzffvt5h4i2tdeWp+GLr7766mCYQi3bUvzmM1rGN5RLtBzLlVtA/wlUT/Nzzz03eOFDf5u1rLw999yzcuNw9gggkFcB/T3SKBXRIdRKogGzZ882p512Wknsin0ggAACCCCAAAIIlLBAnz59UvaoF+H17EmjdWnEtPXr16ds17MwBTuPP/74YES46LRSPXr0CJ6jpFT430r37t3TeioqqKb9aMS0iRMnJkcq0X2p1v/1r38FI88pkKtOBmHSc8TnnnvOaKheX/r2229TNr/11lvBSG16bqlR9aLD/qqgXvabO3duSh1fxwgVzPUYqpNPC52T5m2NplyCl/HnXnpumsk9PFa8rvJzOXa4Hz4RQACBQgv4/7UpdOs4fokL6B+7d955J3hrKz50Qq4HU7Dq3//+dzD8aS511UNUw8oedthhZvr06blUTSmroYd1I6WhWjOl+NtS0fLZ/gNu+8c/3E+mfWjuWN0AZkoKvMXT0BtaAAAgAElEQVQDdhoC+amnnjI//fRTpurBdk04ryGTL7/8cvPaa6856yjgrJvD1q1bp5Qp7nkW2lhvHWqo44svvjiYrzflpLJYUeBbvf70vfSlkjhP3/7ZVvEEfvvtNzNlyhSj/7Dp54MPPjDxoZHCs840Z29Yjk8EEEBgYwU00oX+5sQfFG3sfsP6L7zwQvD3TlMIkBBAAAEEEEAAAQTKjsDee+9t9Hxw1qxZKY3SyFv60XMlTeGgUbWWLl1qFi9ebB25S9NsPf30094RwO6//35z4IEHmmiwVXOkasQ0JT0Dq127ttG8mq6k0UzGjBlj1HkjU9K+4qOMaaS2cFqs+LO5mTNnpgWKMz3jy/UYYZvzZaFzij9zyHROYRv1GX8umEvd+DMzdcDQd4mEAAIIlDcBepyWtytWAu1VjyYNW3v33Xcb9ZTMJSnoqd6dCr5OmjQp56BpeKxtttkmmLP0vvvuM1rONumGSj0jdOwJEyZkFTTVvuP/6EePl+0NgG8fmd5GO/nkk4ObtHiQMtoOLdv2ownkP/7442De13j56Lre/tLQH5qL4MorrwxuXH226lX66quvRncRLPvO02dV3HrRBvj2YbOJ1tWyhofRDeJRRx0V3+Rc11yveutwwYIFGYOm2omvjT4fZwPYUOEE9B8UfQ/VG1//kdR/qvSpddt/YEIABf6jQ3WH+XwigAACJS2gIXV1z6C/Sa6kB1O6f9HDM80jr3uvl156Kfg3Uz0Lsnlw9cADD7h2Tz4CCCCAAAIIIIBAAQU03VbVqlWtLdC9onosKsiokUnigUhNlXTKKacEwUwFWX1JHQT0Qp2rnHqVuoKmm2++ubn11lvN+PHjjYaLzSapk4Urqd3xUZ7iz3hkkun/5bkeI2xPvizi56RnEq7R48K2hZ8ahU5TVEVTLs+64sdmZLaoJMsIIFCeBOhxWp6uVgm2VQFQDcGh3nkKtL388svB53fffRcM06ohMrbYYgujoXD1hpneRNNE5nrLS5O+l0TSDcsFF1wQDFGph3EK4n366afBP9D6R1o3Tzq22qDglm5M9KP1XJMCY/rZmKR5HPRT3KSHj/r55ptvgqGDV69eHfSMVDBYN4Dy3m233ay7V6/aN998MzDSG4HqHaJ5EfXm1k477RTMTXbiiScGPS6jO1Cv4OjQJtFtruWBAwca/eSayoKx2ixDfZ/nz59vxo0bFzzo1fxt+k7pIa++UxoCWYFYBeF9c73aDLRPEgJxAf0+hj1K3377baO/obkmDT0U73Ge6z4ojwACCGQjoEDo5MmTnUU195R+4vNfhRU0vLj+be3cuXNwPxLmxz99gdl4WdYRQAABBBBAAAEE8iegkUf0UpxeQI/P2elqhf6/2rNnT3P99denjV7mqqN8HUsdOM4+++y0+TNt9WrWrBkMbXvDDTdkHfAL9zN48OCgh+y7774bZiU/1ZlB+46meKBPz5QUsPWlXI8R3Vc+LOLnZBvdLtqm6HK8rrZlGzj98ccfzWeffRbdXdZ1UyqxggACCJQBAQKnZeAiFLIJuunRP4DZ/iNYGm3VW2cKYOmnMiQNCeubD9VnoN4htgnlfXUq6zYNBaIfBedJCJS0gAKjCpAqWKq3X7P9j2bYDr0oEX9rt6Tmfg6PwScCCCDgEvD1BH300UdNfN4r2340aoleJtKc6itWrLAV8QZVrRXIRAABBBBAAAEEEMibgKYqUo9S9QhV71INs6oOFT/88EMw1GuTJk1M06ZNg84M+tRISu3atStW+1Rv2rRpwTQ2eolPnQI0V6j+X7zDDjuYhg0bBs/K1GHjiCOOCDoXFOdAGnlN/1dXz0l1ztBQw1tuuaXRaG62nqSjRo3K+TC5HiN+gNK2KM45hW084YQTjH6Kk7beeuu0IYKLsx/qIIAAAmVBgMBpWbgKtAEBBBBAoEwL5DJPqe1E1Mtfb3mGL4moJ/hBBx2UUrSyvDySctKsIIBA3gXmzJkTPLCyHVhvwGcTNA3r6gUlvdD1+OOPh1kpn+vWrUtZz3ZFf3M1l5amFdCDO/V8VaBWP65h3rLdt62chofTiCAanUIP7jTSSTZDEdv2VV7y8m1sc1HAfdGiRcG0HXowq38rSzrpO6jrqu+SXhjVcTRcXWmksmBaGufFPhFAAAEEKraApn3amGBZLjr6t1gjo+mnNJNGuNP9nH5KK23sMfJlUVrnz34RQACBii5A4LSiX2HODwEEEEAgZwHNU/rBBx8kh9+dMmWK0QPRXNKuu+6aDJQqSKq3L8OkIYeiScMB5TrndLQ+ywgggEC2Ar7epn379s12N8lyruF8VUBzN2eb1JP/scceM2+88UYQ2LX9zdWcU926dTO9e/cOhm/LNIya79jq4aDetXojf/bs2SlF9SBMQ/nfcccd5vDDD09uu/3224NeGMmM/y3I4Nprr41nm6lTp5phw4al5SujRYsWwXDI1o2RTA0z9+STT0ZyihY17YCGvMs2laaxgs833XRTWlM0pcSll14a5Ovf1ldeecUMGjQoGK7vp59+SpZXQFwPN9WD+ZZbbtmofxM1xNyYMWOCaS70b3l82gr9e6wAatu2bc1ll11m7X2SbFiGhdI0zXBoNiOAAAIIIIAAAggggAACCJSSAIHTUoJltwgggAAC5UsgOk/phAkTjB4C55I0d+7BBx8cBEs1hJFvSG7NWRxNDNMb1WAZAQRKU0ABJVfSsGO5pj322MM53YKCg5mSAk8KUGqu9J9//tlb/I8//ggCbwq+tWrVymiYN81VlUtSQPbuu+82CoK6jqcA38cffxwME9erVy/z1FNPGfVc1HxeakM8aTg5W5K1grO2lG2QWufo2ke2vSjyYTx9+vQgIBo/Vw0BqMDpO++8YzQ3ruagt6Xff/89GE5PQ+rpnG+++WbTv39/oyB2tum9994zN954YzCEvq+O5t9SsFw/I0aMMMcff3xQL5fvUj5MfefANgQQQACBkhfQqBxr164t+R1b9qjehjVq1Mjr8Qo5RZeFgKxKJsDvVyW74JwuAhVAgMBpBbiInAICCCCAQO4Ceui5MfOUap6U/fffPxkozXauFz2wVW+YaCJwGtVgGQEESktAQUP1tHQlBb/0dy2XpN6f+ilOUjDt1FNPNUuWLMm5+rx584weAI4ePdpoiOFsko7TvXt3M3PmzGyKB2UUWNtzzz3NPvvsYw2aqpDrQWT8b330oK460TJa1lxjrpTNPvJl7GqnXiJSL2fNOb9+/XrXqaTkr1mzxlx00f9j70zgbaraP/6kIjLLnCTKWOb3VZnjRRlLg0KkEipK5U0UUikVEqUBpUQU0msIJZn6y1wyFDJmjAwhuv/Pb72t8669zt77DPfcc8699/d8Pvvuvdde43efu88+61nP8/RUk8lPPvmk45rbydmzZwX5XnnllSDrUrf8ZhqsUaGonTFjhrJ2tt3om3n1cbyY6va4JwESIAESSHsC+D7AO9Bvv/2W9o397XkCruTj1R68IWFxEoUEEkGA/1+JoM42SYAEUkuAitPUEmR5EiABEiCBdEEACgO43J0/f76yRlmzZo3AqihcMeOUwqIUk+hwGxmpwJrVnDyGq8m0jvESaR+ZnwRIIGMS2LNnj69iqW/fvpIrVy4V5zStY3y+/PLL0qdPn4iew/ZdgVUIYrJCiZo7d277suMc1oWwfty7d68jPZwTWKc+8sgjnlndFJh4zvspaN3K2A3AytV2I6zzwFIFsbP9JJ6MvZTE4P3222/7ddPzGtzawzLXdHVvZ4a7X7hTxvd7agSWxLA83bZtm2978WSamvGwLAmQAAmQQGQENm/eHDclJnoGLwfTp0+PrJOpyF2rVq1UlGZREkgdAf5/pY4fS5MACSSGABWnieHOVkmABEiABNKYgBmndN68eQIXfm4x8/y6oeOUQlFav35938lUv3rMa4jfZ0q9evUkNXH6zLp4TAIkQAJ+BPwUUCgHZR8UVVBYtWnTRika4YI8Z86cftVGfA0xLh9//PGIy7kVgDIYCt/XX3/d7bJKO3LkiLRu3ToqpSkqOHjwoHIn7NWAmxJ0w4YNnu73oOQtV66cV3WBdChN4cLWTfD95Hc/483YS3E6Z84ct+6HlYaxw7X9rbfe6pm/S5cuqVaa6sph9fPee+/Jww8/rJMc+3gzdTTOExIgARIggTQlULBgQVm4cGGatmFWjvAIvXr1MpPS9BixvSkkkCgC/P9KFHm2SwIkkBoCVJymhh7LkgAJkAAJJBUBHacUilK44Y02TikUpY0aNfKNUxrtwNE3U+im16TBYxIggbQkUKBAARVP6+TJk77N7N+/X8aMGaM2WNb/85//VItH4MYU1vbZs2f3Le93cdKkSb4ThRdccIGgnUqVKkmhQoUECkhY6kNB6iVjx46VoUOHevbr3nvvle3bt3sVD6RDEQklsZtlLiwb3aRMmTKSP3/+oEteikRkrFGjhsBiNJR4ub9FOTdlra4v3ox/+uknz+9buNHVAhf3+PyUKlVKWeOuW7dOzOs6n7nfsWOHeeo4HjlypEyZMsWRZp/g/kDJDOvdVatWefZTl/vss89cFafxZqr7wz0JkAAJkEB8COC7HAtaKSRAArEnwP+v2DNljSRAAmlPgIrTtGfMFkiABEiABNKIgI5TCmUkXPDCxV4kouOUakVpuHFKI2nDzLtly5agPlJxahLiMQmQQFoTgDvSCRMmhN0MXJjCDSq2wYMHC1z4wt0bLFHbtm0r5cuXD7suxFeFRauXXHfddfLmm2+K/SyGxShc8k6bNs216B9//CGIOwmXrbYgvibioHoJXBM/99xz0rRpU4GSDQpNKJbhXrZfv35y/Phxr6Iq3UuB6af0hCI6HPFTvnq1mwjGfv3EOGFdO27cOKUwhmJcyy+//CLVqlXzVWbi3roJ2nzsscfcLqk0KKeHDx8u119/vSMPXO36WTtDCWxLIpjafeA5CZAACZAACZAACZAACZAACZBA/Aj875dr/NqMaUuLFi2SHj16xLROVkYCJEACJBBfAniWhyM6TqlWlEYbp1QrSqONUxpOX93ywOWgKUWLFlVWVWYaj0mABEggLQn07t1bJk+e7OkCNlTbsNz7+uuv1fb000/LNddcI3fccYd6Hw8VZ7Rbt26e8cMQ6xmWpW6xo+HO7oMPPpDLLrtMsGDGTeBez1acwqLx0Ucfdcuu0qCoRb2wgDQlR44c0rNnT7nyyivlpptuMi8FHXspQf2UiV5KT7tyP+WrVx3xZow++421cOHC6rMC62FbSpYsKaNHj1afH/uaPkd5W+BNAu57vdwYN2/eXD7++GNXC2QoW8F16tSpdrXq3C0GbiKYunaOiSRAAiRAAiRAAiRAAiRAAiRAAnEhkO4Upzt37nSA+f777wUbhQRIgARIIOMQ0M96HadUK0qjiVMKSxe43cUWqzil0ZK2Fae0No2WJMuRAAlES6By5crK4rRdu3aCZ2xqBcpJbFCAwVrUS9G4YsUKmT59umtzxYsXV1ahbkpTXQDKTFidwiWvmxw9ejQouXPnzp6xrUuUKCGzZs3yjRMKRWzt2rV9Y2i6KTBhser3+8StjN15xNuElwI3gdUv7qMtiWCMPvgpTqG4dlOa6r57KZ71dVgB2/Lss8+KlwtfxHCDMtzPnXSVKlU8Fad2zPFEMbXHzHMSIAESIAESIAESIAESIAESIIH4EUh3itPdu3fHjw5bIgESIAESSAgBTMLDmiTaOKVaUZpWcUqjgQJ3l3AlaQoVpyYNHpMACcSLwG233SaweH/wwQeV0jMW7e7atUtg6Tds2DDXGKZI95I+ffpIwYIFvS4H0v3y2C5dobhETEs3gTtexEVFTNNQ0rJlS0/FadasWaVq1apBVaDdc+fOBaUjAUriYsWKuV4zE6Gw8xIo/tC2LfFmjPbx3bZ69Wq7K4Fz2wo4cOHvA3iS8JOKFSs6LkPZD4tpL4F73lD3FVbSsGJ2kyJFijiSE8HU0QGekAAJkAAJkAAJkAAJkAAJkAAJxJ1AulOcYrLBaxIk7vTYIAmQAAmQQJoQwIT+559/7mkpZDaKOKX16tULWJXasfHMvIk8Xr58uRw7dizQBUzcQ7FLIQESIIFEEIBr3LVr1wos+sePHy8zZ850PKOi7RPiR8JKEzEmtezZs0emTJmiTx37AgUKSJcuXRxpXicpKSlel4LSP/roo6A0nXDnnXeG/fz1U3JCAQfrT1v8LDDDsTZFfZG66U0EY/QTC528lJ9wrWwrPm1WbjFFdR5Yj9rWqnDJ7OZOF2XgWrlFixa6uOceeWDRG0oSxTRUv3idBEiABEiABEiABEiABEiABEggbQmkO8Up3GqZkj9//rBWbZtleEwCJEACJJBcBDA5iZhlWqpVqyaInde6dWvXGGa1atWSRMUp1X2MdG+76YXFkD0hHGmdzE8CJEACqSWAZyk2HUN6/vz5ytofVoRnz56NuHqUGTlypLz33nuBslDKetUFBRaUp+GIVx0oe+mllzqq8FOc3nXXXY68fidYnOMlXkrQWChOI60jEYzBxa+fzZo1EywS8hM/a1U3vn739f7775csWbL4NRfRtUQxjaiTzEwCJEACJEACJEACJEACJEACJBBzAulOcWoTuOOOO2TUqFF2Ms9JgARIgATSEYEePXqo+HhmlzHhOnXqVLnllluUK0Dz2rXXXiuDBg0yk5L+eO7cuY4+NmnSxHHOExIgARJIJAHEdtRuztGPEydOKKvHb775RhYsWCBLly71dD9r9xseA0yZM2eOeeo4hutVL4tFR8YQJ6VKlQrkgIX/tm3bAufmAdy43nDDDWaS77FfmBCv+Jx+ykQ3ZaBbB/zqcGs33ox1n/0sY5s2baqzee4jGeeZM2dULFyvysJpz6usW3qimLr1hWkkQAIkQAIkQAIkQAIkQAIkQALxIxC7Jbnx6zNbIgESIAESyCQE4E4PscwuuMC5zgcxx/7973+nGwqwpl25cqWjv4xv6sDBExIggSQjAEvLhg0byjPPPCOLFi2SgwcPysSJE6V69eohe4pn3unTp1U+WIlC8ZrWAit+LX5WifhecYsPqsva+61bt9pJgXM3JeiBAwc8lbawhjRdGAcqsg62b98u+/fvt1L/e5ovXz4pU6aM41oiGOsO+Ck+r7vuOp3Nc+9X3uaLBUheLnbhUrlSpUqe7UR6IZFMI+0r85MACZAACZAACZAACZAACZAACcSWgHMmOrZ1szYSIAESIAESSDWBNm3ayJo1a2TAgAHK0kTHuHvxxRdl2bJlMnDgQKlfv36q20nLCsaMGSOwqtJSoUIFQXxBCgmQAAmkNQFYc8JSz02gQISlaTiSN29eadeundq+++476dChg2zcuNG1qFknntNmfGezwCWXXBLkYte8Hu5x7ty5pWrVqio7FF4TJkzwLNqyZUvPa/aFo0ePKmWxnY7z8uXLS9myZYMuTZs2LShNJ8DSFX0NJZMmTfLMAhf2tvvbeDPWnfvxxx8Fm5tAaRrKHT2smL0UxFCE2pa177//vltTKu3GG2/0vBbNhUQxjaavLEMCJEACJEACJEACJEACJEACJBBbAlScxpYnayMBEiABEkgDAhUrVpQpU6YIJk07d+4cUELCCqpBgwby/PPPy5NPPpkGLcemyjfffNNREeKw2Va0jgw8IQESIIEYEcAz00sR16VLF3nnnXcibglWk3Dr66U4rVy5smTLlk3V62dRiFijw4cPj7h9vwKbN2/2tEpEOdMy1a8eXBs3bpz8+uuvrtmwqMdWYCLjZ5995pofieEqbd944w3POm6++eaga/FmrDswffp0fRi0B59Q4qdkbt68uVx44YWOKqDM9BLbOtUrX7jpiWIabv+YjwRIgARIgARIgARIgARIgARIIO0IUHGadmxZMwmQAAmQQIwJdOzYUWBNdO+994q2PEUTffv2VROsjz32WIxbTH11UCzs2LHDURHd9Dpw8IQESCANCdjPH7Mp7U7XTAv3GHFEvcS0/tu1a5dXNoHr2ljL3r17fasMd8wnT54UeDbwEtsaUufbtGmTPgzalytXLijNTkBMWb975qYgjDdj3Wc/5aIXH10We7/ybuPct2+fWdxxfP755zvO3U5+//13eeihh1xj9UIJPmrUqIBFcKKYuvWbaSRAAiRAAiRAAiRAAiRAAiRAAvElQMVpfHmzNRIgARIggVQSuOeee+TPP/+Ubt26OZSnjz/+uFKe9uzZM5UtxLb4F1984ajw0ksvVS4eHYk8IQESIIE0IuBlMYnmNmzYEFWrsDSEu14vMa0N/eKJpoXiNJQ1P5Rv4cTCfOWVVzytTTFuN8UeFvYgPqmX4PkfSgYNGuSZpWTJkq7ub+PNWHfw22+/1YeOPZSY1apVc6TZJ+fOnQuK/W3mcVO8uln46jJQdIeSwYMHK88VbvmwoMl0o5wopm59YxoJkAAJkAAJkAAJkAAJkAAJkEB8CVBxGl/ebI0ESIAESCAGBLp27aqUp7AcMaVXr15Kedq9e3czOaHHtuKU1qYJvR1snAQyHYH8+fPL1q1bXce9atUqgfLLTUnlWkBExaTs16+f12Vp2rSpXH311YHrOXLkCBzbB/PmzVNeBPyUnVCwYcHM2rVr7eLqHItl4I5Yi5/CC3mg9EWsUT8ZPXq0iqvtladEiRJSpEiRoMtQmkJ56iWImeon8J4wf/58zyxuylpkjjdjtAmLTC/rXrjXv/jiiz3HgQtQ2p84ccI1T86cOQWxwG3xU7QjXuqDDz5oFwmc//zzzzJixIjAuX1gL7pKBFO7TzwnARIgARIgARIgARIgARIgARJIDIHY+8dKzDjYKgmQQAQE4F6vdOnSjs1rQjKCapmVBOJKABOkw4YNC2oT6W+//XZQeiISzpw5IwsXLnQ03aRJE8c5T0iABEggLQmEsnJEnOjx48eH7AIUmK+99ppcddVV8sMPP7jmh2JryJAhjmtFixZ1nJsn69atk9dff91MchzDJTtcsyO+Nd5T7G3nzp1ix/xE//wEMV39YmUOGDBAevToEYil7VaXl6J59+7dbtkDaYjH7SZ//fWXYEHQCy+84HY5kObVbrwZo0ORutkNDOLvA7/yiKHrpiQtU6aMXU3gHLFlEd/WTfDZxcIqfCe7SfXq1aVZs2aOS4lg6ugAT0iABEiABEggHRPAdzIWxukNccsPHz6cFCPC+6M9H+b1bsi5s6S4ZewECZAACSSEAC1OE4KdjZJAYgksXrzYYX2Cl9iyZcsmtlNsnQSiIICJULjtfeKJJwKlMdGOCWj8SDOtkAIZ4niwZMkSh0UNJoJDWTrFsXtsigRIIBMQgAUorCy95I8//lDPSihPr7nmGqUYxTsBLO5gIQnXtnDLC+vQLVu2eFWj0qF0rFy5siNPqMUi/fv3V/nhfj1btmzqGM/xr7/+Wp566imBJaGXDBw4UPLkyeO4XKBAAfVO4xVr9NSpU9KoUSPl7h3KMliOfv/994J3I7S5fv16R31uJ16Wn3Zf7LJQ7t11111KSQerXFhdYnxfffWVpzLarMOr3XgzRp+83PTimlc/zbH4KU69yletWtWTE6xXcT9fffVVAY+LLrpIDh06JLCq7tOnj6xevdpsPnCMezZ58mSx3QAngmmgUzwgARIgARIggXROAN/zWLikBYuf4AUlGQTvMKY3FrwDeMWh59xZMtwx9oEESIAEEkOAitPEcGerGZwAJh/N1XSYiMQkXbKIPdmF/mGCiUIC6ZEAYptCeYoJdi3aSgmLAtq3b6+T476HlZQpxYoVUz/S8uXL52pNY+blMQmQAAnEgsCdd94pjz32mBw/fty3OigNsUUrt956q7i58EVcTihTvTxboF+PPPKIUm5dfvnl6n0ECloodP3k4Ycf9nTN2rZtW3nuuec8iyMeJmKYYotGvBR7l1xyScjqJk6cKNgiFb+4oYlg7Kf49LKMNcfsV96Lb/PmzeWDDz4wq3EcYxK0devWSgkKd7/Hjh1zXHc7GTdunLI6sa8lgqndB56TAAmQAAmQQHolYM85eX23J2J8dt+uvPJKwe9zN7Hzhpo7S/a5QLcxMo0ESIAESMCdABWn7lyYSgJRE4AbsAceeEBOnz4dqAMTc8msOK1Vq1agrzwggfRIAHHhoDyFtZMWuD7s1KmTsjy94447dHJc91OnTnW0h5hw+NEIiyhYs8D6FDFPQ7nSdFTCExIgARKIgECuXLmUFd79998fQanIsnbs2FG5SLet9nQtI0eOlPr16/u6v8X7k5erVV2P3sNqc/jw4fo0aA9FLNwKh6M4Cyosor43sADHtJTQ+aDAhCtZNylcuLB6vsPSMRqpVKmSbNy40TVOaqi4ofFkjO9XWCG7CWKboq9+AqU4rHy9xEvxesstt0jx4sUllEtk3Ltw7j2U623atPHqhsSTqWcneIEESIAESIAE0iEBe4FUMilOI+mbrTj1mztLD3OB6fCjxC6TAAmQQMIIMMZpwtCz4YxKYM2aNQ6lKcbpNQGUCAZ79uwRxHQwJZn6Z/aLxyQQCYFnnnlGoEA1BZPeHTp0EFuBaeZJq+ODBw96WnhhUh0WR126dJESJUoIXDbC7fAXX3whsISikAAJkEAsCdx3333SvXv3WFap6oJV/+DBg+W9996TrFmzetZfp04dV2tUzwIeF6CYxXMe7XkpaVEUi1OitSZFecQadVOa4lqFChUEykE3AY+HHnrI7VLItCpVqihl79mzZ13zhnpXiyfjH3/80fP7rVq1agLlsp/Afa7XOBFb1GsxEVzw+ynM/do0r6EexLq13xnMPDiOJ1O7bZ6TAAmQAAmQQHolAAUi5sVMCfUeY+ZNy2O8361cudLRhJdSN9K5s2SfC3QMmickQAIkQAIhCVBxGhIRM5BAZATs1WuYnMEkUrII3Mjt2LHDsd12223J0j32gwRSRQDWI0R+3fEAACAASURBVHDdawomZ+GqcsaMGWZymh8jHmC4AsubESNGKCvUvHnzKivUl19+WcVkg+UMhQRIgARSS2DUqFHy5ptvxsw1f8OGDdWkmOkm3a+PiEf6+uuvK2tOv3xe16BM+89//qNc8IZSzKEOKIvN+Nde9ZrpsM6Fi3U/i8lQE3+wdr3++uvNan2PoQDu0aOHfPPNN/LLL7945vWa1DMLxIuxbX1h9iGcftrvypGUhxvmQYMGmUUiOkb83rlz56qFS+EUjBfTcPrCPCRAAiRAAiSQHghAgQjlqRYsLMMCsWSQH374QRAX3RSvd7tI587s95tkmws0x8xjEiABEiCB0ASoOA3NiDlIICIC9mQSYiBkz549ojrSMjMsQmDhZm7ZsmVLyyZZNwnElcBLL72krDfNRuHGFwsEMOkeL4H1qCmI3Yf4KaEEfYXSFQpgLLqA60e4wURct71794YqzuskQAIk4Emga9euggkjWLjnyZPHM5/XBVha4nm0cOFCWbBggbK+9Mrrlg4FIVb5t2jRwu2ya9oVV1yhFL4///yzNGvWzDWPV+KLL74oH330keTPn98ri0rHxBZiY2KiD14K7Ikvs3AoxWDu3LnVMxxjhSLWS6AwBYclS5YohTJicq5YscIru3Lz7nnRuBAPxqnhg676lfeavDSGKP3791ceGkqXLm0m+x7DxS/cN69fv16g9I9E4sE0kv4wLwmQAAmQAAkkMwH7ez5UXNB4jsXuG+bCKleu7NqFSOfOkn0u0HWQTCQBEiABEvAkwBinnmh4gQSiI2C/LIWaYIuuFZYiARLwIzBs2DAV8xQWVlqw6hXx0WB5iviiaS22xWnv3r3lwQcfFMQ5nTVrlpr0hfIhVCy8AwcOyIQJE9SGPmO1LuKiYoNV00UXXZTWQ2H9JEACGYgAFJF4RsJCf+nSpUqJBYXdr7/+KkePHpUjR45IlixZBNbv2IoVKybVq1eXmjVrKuUdFHypEUyeffbZZ7JlyxaZM2eOLFq0SC0KwbMOk1cFCxZUC0agQEN8eD/rz3D6gRjXrVq1Um3Onj1bPYN/++031QaUaeXLl1deCYoUKRKozp5UC1wQCUuBiQVzsK6F4haLaODpAwtfYHGBBTTYrrrqKuVS2Kwb31nm95Z5LZLjtGYMy2Vs0QqU2dhSI40bN5ZNmzbJsmXL1KIouP/FZwgbPr+4n9gwGdqyZUv1GfZz7xyqL2nNNFT7vE4CJEACJEAC6YVAMs+J2e94+H6PlSFBMo87vXx22E8SIAESSCYCVJwm091gX9I9gcOHD6uJQHMg4aycN/PzmARIIDYERo4cqWKojRkzJlDh6dOnlVXRzJkz1YR84EKMD+B6d/fu3Y5atbIW7ibvv/9+tf31119KaYGJdWz4IQeLUz+BRRQ2WNZCaVq/fv2AIjW1Cga/dnmNBEggYxHIkSOHeg5COZkI0QrEaGOCRtJnKDJvv/12tYVTzp5U02VgcVupUiV9GnKP/G3atAmZL60yxJNxWo3Br164bK5du7ba/PLF8lpGZxpLVqyLBEiABEggcxKw36OSaU7MVm7Gqm+cC8ycn3WOmgRIIGMToOI0Y9/fVI/u4MGDgtX5mNBH3CVYI8CdGVxjlSlTRu3h/rJQoUJRt3Xy5En58ssvldXB5s2bZf/+/QJLgHz58ilrgGuvvVa5MoO1QyTyxx9/KKsCXQaTK7CyMAUKCrSNuFJQcqBtWFcULVpUatSooRQsoay5tm7dKggwD4Hlhi2IiwCrCi1QmoRy3QsmYP7TTz8p7tu3b1dxGGAZgQ11gAfYRCKIlWgrZWD94LYCP178/PoPbsuXLxe459MbFE2wRsE9atCggTRt2lR9Vvzq4bXMSQCf6zfeeEN95seOHRuAcOrUKWV9BLe9UDqmheD/15SSJUu6uumFVUytWrXU9vTTT8vx48eVi0eUx4bni59gLLDYwgaBdQ1cWcIaFa4IU/Ns9muX10iABEggoxLAcxfvv24C9+nhxFd1K8s0EiABEiABEiABEkiPBLD4GPNmmK+DBw3ECMWcFH7jYgEe5ry0YC7PnP9CejRe2PC7eP78+Wph8b59+9RcHcIhIPxNhQoV1FwdFqhFIug3QlaY4tW3cObO0mIuUPctLcZvz/GhLcyRmu+28D4zffp05dUD85GYU8Hcb7ly5eTWW2/19XYFTyDwKrNt2zY1vwpXx/BcA6tezBv7hbDQ4+aeBEiABJKNQEp6ku7du6eISGDDOSX2BLZt25bSsWPHlPPPPz/A2uRuHufOnTvlxRdfTDl16lREHdm3b19Kr169UrJnzx6yDbRXr169lLVr14bdxvjx4x31VqtWLVD28OHDKT179kzJkyePI485Lhzj+tixYwPl7IPjx4+Hxcisd/v27XY1gXOMr0ePHiH7peurUaNGyoQJE1LOnDkTqMPvwGaCevbu3etaxM6bFvxcG05JSVm/fn3K7bffnpIlSxbf+4P+X3jhhSn9+vWL+PPn1TbTE0MgLZ/t586dS+nQoUPQZ+niiy9O+eabb9JkwE2aNHG0d//990fVDp4Xo0ePTmndunVK3rx5HXXq54DX/rzzzkupXr16ypNPPpny1Vdf8X8kqjvAQslIIC2fF8k4XvYpvgQ++ugjz2dt796949sZtkYCmYQAn+uZ5EZzmCQQIwJ8ZsQIZIhqNm/enNK+ffsUzPl5/ebEfB7uB+bYIHPnznXkxZzaX3/9FaKl/11euXJlSqtWrVKyZs3qqMduH33q1q1byv79+/9XOMTRokWLgurcuHGjayl7Pgztm3NnsZ4L1J1Iy/HbY8qRI0fK2bNnVdM7duxIeeihh1Jy5swZxEizL1WqVMrMmTN1VwP7WbNmpVSpUsWzHMpj7mXUqFGBMjwgARLImAQy0vcznl1Zkk2Ly/4klgBWVT377LNStmxZef/99wOWlH69+v3336VPnz7KQtO2ZvQq99prr6mVTcOHDxesegpHvv76a9XG1KlTw8muVqaZGfVKMliewc3WiBEjVCwvM499jNVW99xzj4wePdq+pM5XrlwZFiNdGNZfWJVny9mzZ+Xuu+9WcZgQ2wrthiPfffeddOjQQW644QY5duxYyCK2y5QSJUooCzW3gnbetOBnt4tVdXClhxVpkydPFliXhhJ85gYPHqxWOob7+QtVJ69nLAKw6hw3bpy0a9fOMTCsOL3xxhtVfDTHhVSeYEUuYvaZAgvQaATPi27dusm0adOUBRSs4/v37y9wKQTrfz/B8xzPqBdeeEFZZ+fPn19Z78OFMVaDUkiABEiABIIJ2O8/Zo5YuXMz6+QxCZAACZAACZAACSQTgTNnzsiTTz6pwhN88MEHgjk/L8F8HubL4O3oyJEjQfNw8OTm5uHMrg/ePmCViPwzZswQ9MFP0Cd4l6pataosXrzYL2vgmu2mF17uEHPeTez3QXvuLFZzgbrteIzfHpP2pPLxxx/L1VdfLZgnwJycl8CSFCEoEF8eAuvim2++Wc2pIJSQn2DupUePHp5zq35leY0ESIAEEkWAitNEkU/CdvGS06JFC4G7yFAvKW7dR0w/vLj4CVzQ3nnnndKzZ0/l3sMtr59rXCjG7rjjDoGLzVBivxThpeDhhx+WLl26yKFDh0IVd1zXLjQdiSJBL4X2dfu8Zs2adpJSHLdu3VopqoMu/p2AOGR+AmVK3759/bKoa/aLkt8EYDz4mR3GCzc+f3hpg8LHFrg39nvhxstyOAzsenmeOQjA/cyECROUexlzxFhwAHfPK1asMJNTdYz/R3NBCNrGD8nUCupBLLdBgwYpF9Z4jk2ZMkXuu+8+1wUZdnt4/n7++efqOQhXO3CvhLKow8stpV0Hz0mABEggoxOw35XM8epFZGYaj0mABEiABEiABEggoxDA72MsLh4yZEhE84JQnEExZs8jhfPuhHcvzNfhd6nbXJAfW4Tcwm/t1atX+2VT1+x3PD+lrp3Xnjuzr4dq3G0uUJeJ1/jd7g3mFmC8EK7xBow++vXrJ3v27JG6deuqBd56HOHsBwwYIJiXoJAACZBAeiDgb66SHkbAPsaEAGJ0YqXQV1995agPMQRgCXnLLbcoC1EorxDXYP369YIvPMQ8NQXWqp06dRKUswUvQFhBZis9CxcurL6o8UKFDf7zEbcP8QJg9TpmzBjHlzj6CgusjRs3ipdCEeXXrVvn6MIrr7zisLJCbAQoYeHTH1ZdBw4cEFi1on94CTAFCoq5c+cqDmZ6lSpV5NVXX1VJYPHSSy+Zl+WBBx5wrGCz47TihaFJkyZBK+Sw2uuJJ55QsRuwAi5nzpxq5Rf6hdV3sEpFzFlT3n33XWVFC+s6NwGTtWvXOi55vcTGi5/uDCz0oDxeuHChThLEq8BnD58nMMiTJ49SRiHWKT6DeMGzx/P222/L888/L4jbSiEBmwAUjxMnThS87MOCUwtWq8IidMGCBeoHm06Pdm/HN8WPJKxmjbXgOdu2bVu1oW78b8yaNUvFRoXFq9/KYOTHj8x33nlHbXhu4IcjnkdggR+G/D+K9R1jfSRAAslOAN8Pq1atcu0m3lfdvIa4ZmYiCZAACZAACZAACaQzArA2bNCggfJaZHcdv2mx4TdjkSJFlBHBkiVLZN68eYGs+K1t/4b0mnPShfC7tXHjxkFK2kqVKqk5Isy5VaxYUbZv3y5oT2+msQcMLDB3BI9siKvpJbay06tv4cydpXYuUPcxXuN3m+NDPFLEMdVy/fXXS/369QNzIlhcjnu6Y8cOnUXtMW+M+YJdu3YF0sEDnx3cN8zdIc4t5howR2EK5l2x0BzzDhQSIAESSA8E0pVT5YzkKzmZwCNGpPZbr/f/+te/Ug4ePOjZTfj3r1y5clA5xIZyk+effz4o7z/+8Y+UXbt2uWUPpK1bty6lcOHCQWVffvnlQB77YNmyZUH59bjy5cuXMnHixIAvf7vsjz/+6Bp3dfjw4XZWx/nkyZOD2ty9e7cjj33y5ptvOsogXueQIUNS/vzzTzur4xz3pWjRoo6yGJ9f/FQ3JgsXLnTUq0/c8qYlv6efftoxloIFC6YgtoOfIKZup06dHOXQxzlz5vgV47UkJRDPZztiAjdv3jzos5M/f/6UNWvWpJqQ/VzE5zvegmcI/r/79u2bgljI4cQL1v/j2CO2CWLLIA7Jli1b4t19tkcCvgTi+bzw7QgvZjgCq1evDvpu0M/GFi1aZLjxckAkkCwE+FxPljvBfpBA+iDAZ0ba3Kf77rsv6D2oSJEirnEtdQ/efvtt39+ae/bs0VmD9riG+vW7FvaIhYm5Pr85sffff99RRpefNm1aUBs6AfOXOp/ez5gxQ1927N3mw7zmzlAwmrlAlIvn+N3GpDkgdqkXO8yPIk6tzmvvS5YsqeZX3eLYnj59OqVevXpBZd955x0Hb56QAAlkHAIZ6fsZzzt307T0oO5lH2NGAJaUzz33nKM+WPnNnj1bChQo4Eg3T7DK7JlnnjGT1LGbT3xYciIunyloA6urihcvbiYHHWtf+/YF9M9L7JVkOh9iZ8KSALEOYX3mJnBhCWtaW/bu3WsnOc5ttxcYV7FixRx57BM7dipidSJebKjYhbgvDz30kF2dstIMSvw7wWaC8dsWsLqsnVenpwU/WHfAUtSUYcOGBVa5menmcbZs2ZSFr83KthY2y/CYBEAAq2ARKxkuek05fPiwipULt+PRyr59+4Ks3aONbxptH1AO/xf16tVTz3asFIUr3kmTJknnzp3DspjCcxzW7XC3hJjQsNCHBT0sdRHLhEICJEACGZGA1/sPxupllZAROXBMJEACJEACJEACmYsAQrrY8zKtWrUS/DZu3ry5J4x7771X3nrrLdfrCA1TtGhR12uYB4JHOtOL3WWXXaba6927t++cWIcOHVT4GbvimTNn2kmBc7fQPLb7XZ3Zfh/0mztDmWjmAuM9fntMeqy1atWSDRs2KOtenWbuMT8KV75uAm+BcJGM+VW3sFqw/kWsXFsiDZ1ml+c5CZAACcSLABWn8SKdpO3AtUL79u0dcQTwxQn3uF4uX82htGzZUrlhMNNsf/VQJMAlLlzsaoHCYty4cQLlVzgCV8F4iTIFLjrwsuEm9osL8uTNm1emT5+uFABuZcy06667zjxVx/nz5w9KMxPsFxGvlzBdBu4pTHfCpUuXlscee0xfDrm3FYZw53vJJZd4lrP7V6FCBeUC2K1APPnhnphKabyU4gU6HClYsKAUKlTIkRVuQSgkEIoAnj1QAsItkClQMN5www3y448/mslhH8NNrxmXBe50Qz0Lwq48FRnhKhg/eMaOHavcHP3www+CBQrNmjXzfA6YzcE1OL4X4NIdzxm48Rk4cKAsW7bM8zlslucxCZAACaQHAm7vP7rfyfAs133hngRIgARIgARIgARiRQC/gaEANQWL5idPnuxrTKHzd+zY0TVcl9+is8cff9wRsgrzWVB8YsFuOIJQXJgPMgWuer3Eng/D/CLCMLiJnddv7gzl7fzhvDPGe/xu77jgh7iyF110kRuGQJrbXGiuXLnUQutQIYmQz5ZQBiZ2fp6TAAmQQKIIMMZposgnSbt4wcFLkhYo4z788EPfuAA6L/ZQcsHy1AwkbitOEWvAXEWGL+XXX3/drCbkMZS4eFkxfevDRz+soqAQtcV+ccF1+NcvVaqUndX13O3LHbFQvQQK3JUrVzouh3pZmjNnjpQvXz5QplevXmEpq3UBxHgwxct6VOexX5T8XmLjyQ9WcKZAwf7XX3+ZSb7HI0aMkBMnTgTyQPFPIYFwCOBZBKvKm266yRHfef/+/Up5ipi7iK8bidjxTaGEtRc5RFJfWuXF8xQbnjuICYOFHOg7PBBgQYff/yCuLV26VG2wzodyuFGjRio2KmKVhPtjN63GxnozHwF4r4B1NIUEUksA8ZgQo8lNMHmIxV4UEiCB2BPAc5xCAiRAAiSQGAJdu3YVGDxowe9kxLYM19ABHp2wIPmTTz7RVai915wY8g0fPtyR96OPPhIoa8MV/MauWrWq+g2ry/z+++/6MGhvz3H5zYdFMncWzVxgMowfc6yYi4NVcCgxDR103qFDh6r5BH3utTfninWecOdldX7uSYAESCBRBKg4TRT5JGh38+bNDmUBugT3uX4KQrduw5rUDChuKhowAYWJeFOeeuopgXVlpFKyZMmgIlCe2gJ3m2Z/cB2WVrBaDVdgXWWLHxe4L/njjz8cRfxexJAR7pFtF8mOCnxOMG5b4ejXnhsTr/xuedOSn5vVMJQxL7zwgg+B/11q27bt/054RAIREsiePbta2QrLSygPteDHQcOGDQVuxsN9XsHSdP78+boKtU+Em15HB8I4wQ9djBXbkCFDBK5zsLADitQFCxbI7t27fWvBD9RPP/1UbciIZyV4YuxwF0wrcF98vBgFgZ07dzpK4Ts4NS62HZXxhAQ8CKxZs8bjCpNJgARiTcB+zse6ftZHAiRAAiTwXwJYuILfcqbgN2HFihXNpJDHWDBsK0695pxQvyl169b1dQds5jWPbQWcbcSh8+J3uq049VLqus2HeY0D9UczFxjv8buNqU6dOur3v2bkt8e8rilwwevlvtfMh2M3T17hzq/YdfGcBEiABOJNgIrTeBNPovawossUTJ7369fPTArr2C0eqC4I61VT4JLhiSeeMJPCPs6RI0dQXli82uIWu+Cee+6xs/me24pXrMYyFcJ2YXtFGvLXqFHDzhaTc1ia3nfffYKXH1P8XubcmHjld8ublvzc7iteJDFhAtfFXpYf5th5TAKpIXDxxRfLrFmzBNaSsKTUAoWhVp6GY0UJS03Tuh71pAfFqR6v3iOG8l133aU2pK1fv14pUaFIXbx4sXj9INXlt27dKqNGjVIbVgJfe+21igNYwDLe7bmty3JPAuEQCKXMD6cO5iEBEiABEkheAnzOJ++9Yc9IgAQyFgF7XhCuV7t37x7xIOHFyBSvObGNGzeK7VI3mvbQlm284LVgF4q/I0eOmN3zjF3vNh/mNXeGCiOdC0zE+N3GdOuttzp4eJ3A25S9QBWLpN08/7nVYSusMScMr4UUEiABEkgPBKg4TQ93KY36aL8gtWjRQtysOlPTPNx7mAKrT6xOikZsK1DEQLBjGqBe+8UF+aD8iETsFzm41EU9XmK/DISKgeBVD9KxGg7KF4wXSlLs9fHPP/8ssBR2E68Vc8hr9w/KykqVKrlVE3d+tWvXFvuziI5B6Y4NqwihcME9bNCgges9dx0IE0kgAgL4/549e7ZyMWT+v8A9OD53sDy14yzb1dtuerGS0s9S3S6frOdXX321YOvdu7ecPn1ascBYseFHlBnT1R4DLMphyYutf//+6gcW/p+xwb1vrL9z7PZ5njEJFC9eXFatWpUxB8dRkQAJkAAJCJ7zFBIgARIggbQlgN9qU6dOdTTSqlUrgVFFpGIvIPaaE5swYYKjaoTJKleunKtloiOjy4npXhiXCxUq5JIreD4MC3m9Ql2ZcwGozG/uDNft/F7j1h1LxPjtOVIotW+++WbdJd/9pk2bxHaBHK61KSq22/ZTQvt2hBdJgARIIAEEqDhNAPRkaBITjvgCNAVx/mIpeIGwXTpE4i7X7osd07NMmTJ2FnVuv7hcf/31EccYtOvwU0qiUftlIFR+s+NnzpxRSgW4SMGGuuyVc2Z+t2Os2CpRooTbJZVm969atWqeTOyxpzU/rC6EUsV2PawHs23bNhkzZoza8IKHF62WLVuqLVL3MbpO7knAjQBidcK1OBR6ZsxiPHu05anfRJ6tOIVyMKMJ4txoxSfGhniwpltf+wezPX6s9P3444/Vhmv4kazrg1tfvwUqdl08z7wE7O+7/PnzC1YvU0iABEiABNIngT179ji86djP+fQ5KvaaBEiABJKbwLx58+TgwYOOToZriegoJCJ2HEw3BRkW3Npe6Y4dOxYzL2Nec4T2fBjmkeB1yk3svH5zZyhv5/ebC0zU+O05PhgvFC1a1G34QWn2+JABC8vDEcwV2PO4bp+LcOpiHhIgARJIBAEqThNBPQnatC38zjvvPLnxxhtj2jP7hahw4cICP/rRyLlz5wTWlqZ4BY63XwoibRPx/ey2/L7c8aJn++33y6/HoF+a4B7ZtqbVedz2cD8CdxloV0uo9mwmfvntvGnND2N45513lCIXnxk/6zWMe/ny5Wrr27ev4CUWrqJhLU0hgVgQgMsZKEChKF27dm2gSjwTkLZw4ULXHxlY7AA3tqZkRMWpOT4cY1Vvx44d1Ybz1atXB9z6wu2xWxxqsw64KsL22muvqZXN+BGnFalw043FEhQSCEUAsdbhHppCAiRAAiSQPgn06NFDRo8enT47z16TAAmQQDolYM8L4rcwFhFHI/YCWrc5JyyYj2TuK9J+eIV5sue43Pqm24okb6RzgckyfrjaDVdsHvDCFa6rXTcXwX7sw+0T85EACZBAvAhwRjJepJOsncmTJzt6hBeMcL/8HAV9TmyXH4gfGO0k+JIlS+To0aOO1txWwmE104EDBxz5/FZ8OTL+fRLplzvc+kKhZ0qoNmHtC9cgHTp0CPniCDciWBGHmIMffPCBWslnuzv2a8+NidfLiltev7rNMevjSPmhHFb7wWXJmjVrBBMn4VoOwXIa1qddu3bVzXNPAqkmAOu1+fPnB7mzhpvsG264QVlZ2o3AWtxUEiK2JxStmU2qVq0qffr0kQULFshvv/2mYsf26tVL4LIolCAuzldffSVPPvmkej5CKXvnnXfK+PHjZdeuXaGK8zoJkAAJkAAJkAAJkAAJkAAJkEAYBDCHNWPGDEdOLJq355ocGTxO4PLXngdym3Oy5wg9qos6uXLlykFlEWoG80ymuPUN193mw7zyIn+kc4GJGL/bmCKZ47MVp348TMY4tq1VYbBTs2ZNOxvPSYAESCBpCdDiNGlvTdp1DJPZO3fudDRw1VVXOc5TewJ3jHC5ZMqll15qnkZ0PG3aNEd+KDagiLUlFl/M9otB9uzZVXw/uy19bucPFQMB7Bs3bhx0D1AflA54kcALH9yMXH755SoGINxjaoHrWljFmuL38mL3D+W8XpQSwc8cB6yIX3/9dRk5cqR6yYLbVCiw0C8oVbzkrbfekjZt2kjTpk29sjCdBCIicMkllyjlX/369R0W5bAuh/IUCj7k0YLPqim1atUSuP7NzHLRRRcJVrPqFa1w34Q4srDo/fLLL4MWudis8JzDKmi9EhoLSPDch0UqftTjWUshARIgARIgARIgARIgARIgARKIjADmpezYlX5hafxqh1cmc44Kv9OuvvrqoCIbNmxwpOH38rvvvutIS82J27wYvEghPJYpXvNhkcydoT47f6i5wESM357jgzGLV3xXkxGOsTB83bp1jmQ3xo4MxonNB2F6MvsciYGHhyRAAumAABWn6eAmxbqLP/30U1CV4Vr4BRX0SLBjmyJbaixabcUpApm7Bay3v5jLli0rcG0bidgvFpHGNMBLCKxE3QQvk5j0txXXiP36/PPPSzgKbHslX6hVW/Z4ChYsqBSybv1LBD+3fmBMUDxhe+aZZ+T48eOC+BszZ84UfBagmLcFLnupOLWp8Dw1BGDxCAUfYm/C2lTL999/r1wY4RoWcUAyQ3xTPf5o94ijcs8996gNLrkRRxbcsC1btizoB63dzg8//CDYXn31VbUSum7dugG3vlh0gecGhQRIgARIgARIgARIgARIgARIwJ+A25xdtPOCtiUlDALggckWey4Sv7fbtm1rZ4vpuT3HBW9nWJDrJpHMnaG8nd9vLhD5k2H8kSgvYalrGzBEoji15y4jKet2f5hGAiRAAvEmQFe98SaeBO25uTyM5gUJX/pYWaY3cyWSrRjEsBHjNBqBC0w7DkK7du1cq7JfiqL5Yo60Dju/1+o1dPjll19W8fzMzj/77LMyZcqU0jmcjQAAIABJREFUsJSmKDdr1iyzuCrnpxy2++fHJJK8jk4YJ7Gow6hOHebMmVNZlI4dO1YpnR9++GE7S9ALXVAGJpBAFASw4AMKUliAm4KVq7Ac19b1UOiZkhnim5rjjfQYSs4aNWoIYhXjOwSeELAw4sEHHwzrWYhVw7BGf+KJJ0S7mofrc+3OPNL+MD8JkAAJkAAJkAAJkAAJkAAJZBYCO3bsCBoqFrpGKnD5axs6uM05YeGsPU+ImKppLfb8FAwjvAwd7Lxu4zD7a+f3mwtMlvGHGpM5PlsxDG7hWqtCMX/48GGzOomkbUdBnpAACZBAgggELwFKUEfYbPwIuL0kmK5gw+kJJq2hNIC/fC1vvPGGwOrHS6KxBsLLBSbGTYEiA+4zbUFcBcS8NCXSL2a4wT148KBZhadbW2TavXu32swCXm2if++9956ZVRo0aCD9+vVzpPmd4OV24sSJjix+L2eRMIkkr6MDxkm4/LZu3ep4iYJSvUSJEkZN3odQog4fPlyWLl2qYkronG6fa32NexJIDQG4LNKWp/iMa8HzBgpSxB82JV++fEopaKbx2J8A3Bo1b95cbciJBT7arS/cIpuun9xq2r9/v1KaQnEKgbtz3BtstWvXFrgNppAACZAACZAACZAACZAACZAACYirtx7EA41UYG2K32KmuM1RYT7QnhPE7+ZoBHN25lwk5oJg5eomtvLPrW8oF+l8WCRzgag/EeOPdEw2P1sxXKFCBcF8XDhil0UZr7nScOpjHhIgARJIBAFanCaCeoLbdAv2bgdLD9XF0aNHO15UoPSCC0Ytbm507RcWnddvP2nSpCD3F3369BH45bcF7jNPnjzpSI70iznSL3e3/F4vYrAURYw/Ux599FHzNOTx0KFDgywr/cYIKzibiVf/4snvySefVLFcEc8V27Bhw0KO3cyAl05b0WrGmzTz8pgEYkEAnzco8C677DJHdXA/Y/8fIwYqFfkOTBGfICb2fffdp6zx8UN8+fLlMmjQILn++utd3bTbDcAiGM9LLPDBD3K48YaLX9sy2C7HcxIgARIgARIgARIgARIgARLI6ATcXOnarlVDMThw4IDyGGTn85qjsucizUXJdh1e53/88Yfg97aeS8J+8ODBrtnh1ch2SezVt0jmztBYJHOBunPxHn9q5/jsMXqx0+Mz93ZZGOtgcTOFBEiABNITgWDtU3rqPfsaFQH4tLflu+++s5M8z/FyY7+Y/Pvf/1Yx53QhW6mFdMSwi0TwQgQFmymNGjWSnj17mkmB41h8Mdt1IB5oqVKlAm3YB7YyGJaTtmJFl4HSxRbETgxX4K743XffDcru9/Ji9w+F8WLpJvbYo3mxsevw4gd3Lqa4KcLN6/Yx4izYbXXq1MnOxnMSiCmBkiVLKuUplHqm2J9nuuk16aT+GM8HLPjo37+/LF68WFmfwh1Ut27dpHTp0iEbOHXqlMydO1d69+4tlSpVErim79y5s3z00UdBq6NDVsYMJEACmZYAFnDgmWNuWKRBIQESIAESIAESIIH0RuDyyy8P6rI9xxKUwUro3r27QHlqitccEPLY84QI/xWp8hShXcwwYVAA43eim2CeE17sTPGaP4tk7gz12fn95gJ1+/Eev30/4YXJz0ug7if2cLNrx2T1YmeW08c2H1gEuxnY6PzckwAJkEAyEqDiNBnvShr3CS9ICMJuClZXrV692kxyPYZLDFjumG4T4cayS5cujvxXX321IOi6KStXrgyK72leN4+hiGjfvr0jtmn+/PmVq1vbvYcuZ78UIO6dvaJL5/Xa21/uoV4M7BV5Xu5B0J6b2xP7Jc6rXzNmzFCxBKBMNiWUctNmgjiN4Ogmdt605Gcrmuy23fpnpkHhAdcoWuC+uU2bNvqUexJIMwJXXHGFctvrF/+FitM0w68qzpUrl7Ru3Vrg+UD/2IareDwDwomTA8v/8ePHy5133il4diBOCxbpYHGL23M6bUfD2kmABNILASzcQKgBvSFOV9myZdNL99lPEiABEiABEiABEggQqFGjhthWp5s2bVILVQOZfA5GjBghcNNri98cWt26de3sMnPmzKA0r4SxY8cKNlMQ2gtzV25iz+/htx8WQ7uJPSflN3eG8pHMBer24j1+e0zgFK7y0h4fxuB3b/UYsUdoN9urYbhlzXp4TAIkQAKJJkDFaaLvQILav+222xwtnzt3Tlq0aCF79uxxpJsniOcHt4ebN28OJONFCxPWdoxUuKmEdagpmJCGQsEOCG/mwfGRI0fUhPann37quPTWW28pSyFHonFivxRE+sUcjf9/e3Uc6vASvDzY8vrrr9tJjnNYSmFFHZQEprJaZypfvryvcjgSJpHk1e2b+0j4FShQwCwqS5YsCXoBdmQwTqA0haWZKT169Aj7BdAsx2MSiIbAlVdeqZSnWFVqy1VXXeX5Y8zOy/PYEMBioAceeEDwnYHFPVBuPP3001KrVq2QLpOxeAXfbUOGDJGGDRuqhSWItfraa6+FvdAnNqNgLSRAAvEkgMUTcN+tt/nz54ds3p58w4p9v/jJ0bQRshPMQAIkQAIkQAIkQAIxIJA9e3b1+8esCgvcW7Vq5fs76NixY9KuXTvp1auXWTRw7DcPh99ZtkDxGUp5irkwGGvce++9juItW7aUgQMHOtLME3uOy8v7GsrYef3GgfyRzAXqPiV6/KHGpPuJvf3emyNHDuW9yczjdQyLYHtBciRte9XLdBIgARKIN4EL4t0g20sOAojJB4UnFKZaYMGHieO77rpLrrvuOsEKNLjd2LBhg3z22Wcybtw4MS0FoRz98MMPlcJV12HuYcEDS0lToDSF8hXt44sTbhP1Kjes4P/4449l5MiRQQpcvCTdcsstZlWO4+PHjwfFrov0ixn+/22LTq94oLrxX3/9VR+qPSbewA8ruWAVBY7aFQaULbYMGDBA0Hfw0DE6T5w4IXD9NmXKFLVpy0rwhhtg8wUNMRt+/vlnlQ7FJV5+taBe3DtTvMYTb36Ih/v2228HuobPFe7x+vXrBUpQuEc2Y0RibLNnz1ZWYrZCvW3bttK3b99AXTwggXgQgMvzBQsWSIMGDRzuiZo0aRKP5tmGBwE8NxAHFRt+RB89elTwXP7iiy/Utn37do+S/01GTOj//Oc/akMK3DLjnmLRD2Lp2Is+fCvjRRIggaQkgIVsWGxhTui88sorQQv+7M7bE0hYnOEl0bbhVR/TSYAESIAESIAESCDWBJ566in1G8msFy5a8fsH70p414GyEZ7c4KFu6dKlAa8/KJMzZ07Jly+fwzjCbx4OSllcN5WUeB/DXB/c7dapU0fgxS1PnjzKcADzQ8g7dOhQtUDW7Cd+n2H+UM8nmtf0sdkO0rz6Fsncma47krlAXSae40/tHJ/Nrlq1ar6s9Rixt8sizYu9WY7HJEACJJCMBFLSk3Tv3h0O6gMbzinRERg4cGCAo8k0nOMsWbKkfPjhhyEb7tChg28bOXLkSClVqlRK9uzZPfM9+OCDKWfPnvVta+HChUHlN2/e7FvGvjhmzJigOg4dOmRnc5wXK1YsqIzJD/3ScvTo0ZS8efN65i9YsGBK/vz5Xa+j3Ny5c1Oeeuop1+to88svv9RNqb0bk2XLljny6BO3vGnNr1q1ap5jyZo1a0rFihVT6tevn3LVVVd5fj4aN26ccvr0aT0M7tMxgfT6bF+7dm1KgQIFAp/lmTNnpuO7kPG7/tNPP6WMHDkypUWLFim5c+cO3Dfzue11jO+9f/zjHyn9+vVLWbRoUcqZM2cyPrAkHWF6fV4kKc5M161vv/026H9/8eLFvhx2794dVOb999/3LBNNG56V8QIJZAICfK5ngpvMIZJADAnwmRE7mLfffnvQO475ewi/gbJlyxaUJ1euXCl4fypatKjjWqg5tP/7v/9LwTyg2YZ5fN5553nOi+l89erVSzlx4oQvhF9++SWoDcypuYnbfJjX3JkuH8lcoC6DfbzG7zamSOb4MD+peWP/6KOPmsPwPe7YsaOjLOY5KSRAApmDQEb6fsazj656k1GVHac+YUUXXGxEKhUqVJDp06crd7qhysKq0M2Pvy4H6x5YUNqWnrgOi03EsIMFqml9qMuae3tFE1a9ISZBJGLXESqmAeqGha6XZMmSRcXO09dz584tWNHnJbDuxeo+W7D6Dq4ksaKuWLFi9mV1jrZstyP2eBDLwCv2g503Hvyef/55T/e6sNRA3N2FCxcq19D25wPjRQzcadOm+boqdoXFRBKIIQFYlM+bN0+ttMX/WP369WNYO6uKNYHSpUsr9+fwogCXT19//bV6LuP5ieeKn8AyHs/KwYMHq+81xIvGquFRo0bJli1b/IryGgmQQBIRsN95YKmAVfR+Aq8gO3bscGx22AuzfDRtmOV5TAIkQAIkQAIkQALxIICYofA25yX4DWR66UA+zJXBqw9CpuzduzdQNJw5NPzughcxWLG6CUKpuM2LIS9CJDz33HPq9zdcx/qJ7SnkvPPOC5oz0+Xt9za/uTNdJpK5QF0G+3iN3x5TJHN88NKE+UlTIrEYtdu25yrNenlMAiRAAslMwH+WMJl7zr6lmgBeHCZOnChvvvmm5M2bN2R9cJmB4O9waYt4qOEIYp9CqQAlbbhByOFu9pFHHhG47rVjWXq16fbFjPFFIvaLlZdbW7NOKHXr1atnJgWOoWCG6xJTHnvsMZk8eXJI3mB14403qlh9YA7XtRC4Adauf8164fLYbstm4heLy86LF5u05gf3L3DlrMdmjsfrGH3CRCVctkyYMEEuvvhir6xMJ4G4EcCzEa5gmzVrFvR/GLdOsKGICUBZgoU9UITiGYj4qJMmTRK4Eodb9FAC90dQwCIONWLbmrFW4UadQgIkkJwE7Pc9vB+ZoQ7ceo3JvRIlSjg2vON6STRteNXFdBIgARIgARIgARJIKwJQQGLBeufOnUM2gbz9+vVT8zFQpK1YscJRJlzlGuaCvvvuOwlnzg0NYK4Lc2GYB0KYpnDmFu05LoTOgvLQTey8fnNnunykc4G6HPaJGH8kc3w2D/Q53HuLUDmbNm0yhxt2WUchnpAACZBAEhBgjNMkuAmJ7kLXrl3VSxJiSML6Zs+ePWqFFyaIsGIMFjqYFPayVgzVf0w2DRo0SMVIgKXqrFmzVAyEffv2qaKFChWSggULytVXX61eIGCxFWoCy24T8UBTK3gJi1SgcP7yyy8FK7I2btyo2EGZB3ZevKD4Q6wIWEtCCY12MQGPWHrYcO3mm28WWDPZglgPuEdY3YcYE1AkQvHoZuUWCZNI8tp90ufR8IOiafPmzWrFIV7WMdGIz8WRI0dUPF0oIjC+K664Qu0RHxefEwoJJBsBrNJFjBVK+iWAH9K333672jCKH3/8MRAbddGiReo57Te6X375RcaMGaM2eEnAj0t4CsCGY7/4O3718hoJkEBsCdhKzXAngiLpRTzaiKQ/zEsCJEACJEACJEACXgQwhwXL0549e8r48ePVotJdu3Ypz2/FixdXC0RhPHHTTTc5Fq+3bt1aYCEajWBeZ9myZQFjAXhZQ9xQeBuDpzW0i/kxGCqg3UjnCBEXFVs4Es18WDRzgWZf0nr80YxJ9w9zln6eVXQ+tz3mLGGlTCEBEiCBjECAitOMcBdjMAYoN+FyEFtaCV5+unfvrra0aiMR9cK9IxR72MIVWDPhpTQawQta27Zt1RZN+WQrA2VCal7Mkm087E/mJeBnfZR5qaTfkZcvX16w4Vn9559/qh/1c+fOVcrUNWvW+E4SnDt3Tk0EYDJg4MCBAlftN9xwg1ocBEVqJJb26Zcge04CyUcArt9s19rhWjuEO5p4tBFuX5iPBEiABEiABEiABMIlULlyZRk2bFi42VOdD4YAderUUVuqK0tABdHMBZrdTO/jN8fCYxIgARLIiASoOM2Id5VjIgESIAESIAESiBkBuINq0KCB2oYMGaLc+sI9MzZ4ANi9e7dvW7///rvyMgBPAxAstGnatKmyRkW9UKxSSCCZCZw9e1Z5vFi7dq3yroGV5PBIgg1eLxBzKpYCTxz434KrMHii2L9/v/o/gScKhEKAhUO47voR+gGLGSBLly4N6ibil5rKVFg3mFYNsKTA4glT8EzAZJeW1Lah63Hbx5KFrh/WHLAkMQXPJVjLa4GrNXiKgbu1n376SY0XnmjKlSsnt956q+89Rxm4Mt+2bZuyHoEnFri9w0K5XLly6Sa4JwESIAESIAESIAESIAESIAESIIGkJEDFqYhyl3rixIm43CBMsiAuQDzbSwsXZHGBxUZIIAICcHscz/8r/h9HcHOYlQQyGAEoWu688061YWh4/kCJCovUxYsXy8mTJ31HDCXL6NGj1Qare7hohyUq4t1Ur17dobzwrYgXSSCNCUBh+eyzz8qHH34oXrF74VHkySeflPvvv1/gwWT58uXSpk2bQM8Qhx3x7sMRuGlDeAeEjzhz5oxnESw2QKwrWHQj3IOX4L0A4Sa04tQtH1zPmYLwCyVLlgwkvf/++9KpU6fAOQ727t0rRYoUUWmxaMNR+d8nsWZhtgHX8uaY8E6DBR6QnTt3Ktd248aN83RR/swzz8hrr70mzZs3N6tV9w2xx2CV7yaw4H/ppZcynPcZt7EyjQRIgARIIP0R4JxC+rtn7HH6IcD/r/Rzr9hTEiCB/xLI9IpTrCKvW7eu52RQrD8omLw5cOBA3NorW7asir0Z63GwPhJIJgL8P06mu8G+kEDmIwDFELZHH31UTp8+LYiJqi1SEf/ZL/YPLPmgbMX29NNPC2KtNmrUSClSEdfZVOBkPrIccSIJvPrqqwIFGSwe/WTPnj3y0EMPycqVKwXKNlh1IkaVFlu5ptPN/cGDB5UyberUqb7/L7oMlHxvvPGGsmqcNGmS1K5dW19y7NEnP6WpI7OIFCpUKOh/DlavpsB6UitNkR6LNsz604qF2YY9pmrVqqkFG1CoQgEOa1M/gSUplON41l177bXqd02XLl2UZb1fOSiZe/ToobIgfAeFBEiABEiABJKFAOcUkuVOsB8ZkQD/vzLiXeWYSCDjE8iS8YfoP8LNmzfHTYmJnsC9mNeKff+eRncVViwUEsjoBPh/nNHvMMdHAumHAGLdQuE5dOhQgVtTWKbBYq19+/YOZYvXiPCOMGXKFLnvvvsEbknhFvPhhx+Wzz//PKQCy6tOppNAJASgKIUr1t69e0f0mRs/fryMHDlSvv32W0dzoTyfQIkHxR0+936LDByV/n0CN9kNGzaU1atXu11Wrn5dL3gk1qxZM+iKrWS0Y6La14MqsBLc2tBZ0pKFbgN7t3sES9/bb789pNJU14NFH/369VOum7EIVbsi19f99gMGDAhpme9XntdIgARIgARIINYEOKcQa6KsjwT+R4D/X/9jwSMSIIH0QyDTW5zCvdfChQvjdsfy5s0rvXr1ilt7mHSlkEBGJ8D/44x+hzk+Eki/BAoXLiwdOnRQG0YBF5baGnXJkiVy6tQp38EhViA2KKQQV/H6669X1qhw7Vu1alXJkiXTr4Hz5ceLkRGAdSZc19rvxvic3XjjjVKnTh1lkQk3ulC+wVIaCwS0wOrajj1qKxp1XuxhsYiFBrZbXlhwI45plSpVpGLFigL3ufh/0ZuZH/FH7777bvnuu++Uq2CzfpSH5SwEVrBwE2vKAw88oFz56jS4yjYF/5/m+HDNVgSntg3dXlqz0O1gTOvWrdOnao94pIhjqgXPGcSuhUIbsmLFCpk4caLs2LFDZ1H7r776SnB/zXip4IHYzbiHefLkkcmTJyuluFkQ3ne++eYb5Z7cTOcxCZAACZAACSSKAOcUEkWe7WYGAvz/ygx3mWMkgYxHINMrTvPnzy/16tXLeHeWIyKBTESA/8eZ6GZzqCSQzglAqYDtiSeekD/++EO+/vrrgCL1hx9+8B0dFERQaGFDHMECBQoopROUqFA+XXrppb7leZEEQhGAu2hbaQpXu4hnWapUKUdxLAiAIL7pkCFD1DGsEE03r1CiQvHpJrDGhoWjqQRFfsQtRSxMxP/VAsvrpk2bqtMJEyZIx44d9SW1h0vsWbNmKWWreQFur7FB4IbWlv79+wtitHoJFjrg/84UW3Ga2jZQdzxY6DG4jUkrTXGPoWiG0tqUm2++WSmn4cnGvL+wENZKU7gVf+GFF+SOO+6Q8847L1AcdSFWLp51puhyZhqPSYAESIAESCBRBDinkCjybDczEOD/V2a4yxwjCWQ8AjRTyHj3lCMiARIgARIgARJIBwSyZ8+ulEFQVHz//fcCt6Njx45Vigesyg0lhw4dEsR3vOeeewRxF3Wc1Tlz5tANZih4vB5EYPbs2UrxZV6AUhTWiLbS1MwDZRliobqJjp1pX4OC9bbbbnPEQr3sssvU/wFcBJtKU7ssFLZwX23LzJkz7STHue2etnjx4r5KUxS23fCef/75Ylulmo1E00a8Wdhj0v2HUnTDhg1BSlN9HcprKLrdpEyZMspdcrt27RxKU+QFM3yObMHzi0ICJEACJEACJEACJEACJEACJEACyUjgf0u5k7F3YfQJbq169OgRRk5mIQESIAESSFYCeJZTSCCzE4DlW+fOndUGS65Vq1YFrFGXLl3qsMxzYwWLVWzDhg0TxFqtXbt2wK1v5cqVgxQabnUwLXMS2Llzp3InbcYYheXg888/HxYQKDIHDx4scPVripeb3scff1y5+dV5c+bMKVB8hhti4pVXXpGPPvpI4PJVC1z1+omtMPTqm1mHXaZChQqCvnqJnT+cNuLNwlbuYixYqIEYsxdddJHX0FQ6rAVsyZUrl8yYMUPy5ctnXwqcI48tfpa+dl6ekwAJkAAJkAAJkAAJkAAJkAAJkEA8CaR7xSksNLBRSIAESIAESIAESCCjEICrS1i2YYO11okTJwTxBHV8VMQ99ZPTp0/LggUL1NanTx8pVKhQQIkK16JFixb1K85rmYxAp06dxLQAhALzrbfeCpsCFGqwWEQMUlNst7a49sknn8jw4cPNbEoJes011zjS/E5gkYoYv/h/0PL777/rw6A9rDpXrlzpSA9HqWkrGd3GoyuNpo1EsLCVu4hfC8v1cFx9w6WwLUOHDhUolP3EdO+r8/lZMes83JMACZAACZAACZAACZAACZAACZBAIgikO8UpLCgoJEACJEACGZsAn/UZ+/5ydJETQOxHxJrEBoGF4Ny5c5XiCArSw4cP+1aKGIMffPCB2pARSirERsVWp06dkJZmvpXzYromgPiWX375pWMMUNa7WQk6MlknN9xwQ5Di1E05qeOh6uJ169YNfK51Wjh7W/F28uRJz2JYZImYwqb4KUGRD/9TOvanLudXJpo24s3CbUz4/2/YsKEeou9+y5YtjutZs2b1dN9rZvzxxx/NU3VcunTpoDQmkAAJkAAJkAAJkAAJkAAJkAAJkEAyEEh3MU6bNWuWDNzYBxIgARIggTQkwGd9GsJl1RmCAGKa3nvvvfLxxx8rd6WwjBs0aJByz3vhhReGHOO6devk5ZdfVopTuNhs0qSJ6FirIQszQ4YiAJe3psAa+e677zaTwjqGlbMphQsXFsQtNWXjxo1iu9Tt3r27mSXsY1sRmidPHs+ytuUorCxr1KjhmR8XVqxYEXTdT3EaaRuJYOE2pltvvTVonG4Jf/31V5CXH3xX582b1y27I822coWb3iJFijjy8IQESIAESIAESIAESIAESIAESIAEkoVAurM4bdy4sbKumD17ttgTNMkClf0gARIgARKIjgAsTTERi2c9hQRIIDwCUAJBoYOtf//+cuzYMWVBqN362lZzdq2nTp0KuADGNSjOtDUq3PrCzS8l4xKwFadQyEdj9W+7cXVTMk6YMMEBElat5cqVEzeLREdGl5N9+/Y5Uv0+p7biLlSsUlRsl8mRI4dUqlTJ0aZ5YucP1UYiWLgpd2+++WZzGJ7HcA9uu0O+/fbbPfObF+x23T4bZn4ekwAJkAAJkAAJkAAJkAAJkAAJkEAiCaQ7xSlgYUKdk+qJ/NiwbRIgARIgARIggWQlAGVUq1at1IY+bt++Xbn1hWtfxEk9cuSIb9ehAHvvvffUhlirVapUUYpUWKVed911USnVfBvkxYQRWLt2bZDSMlqLf1txarvpTUlJkQ8//NAxVij58fmKhZQpU8azGltxZ/fNraBdplq1aoLYql5i5/drI1EsbOVu7dq1w453bI8PHBo0aOCFI5CO+Mx4BplCxalJg8ckQAIkQAIkQAIkQAIkQAIkQALJRiDduepNNoDsDwmQAAmQAAmQAAkkM4HLL79cunbtKp9++qkcPHhQxaF85pln5Nprr5Xzzz/ft+tQ8KxevVpefPFFFQcxf/78ctNNN8mIESOCFG6+FfFiUhKYOHGio19wuxqtUmvPnj2Ouux6vvnmG/nll18ceWJ54qWAhXLWtmi1++bWD1vJ6Fcm0jYSxcIeUyRKcrss3DCH42539+7dQXj9WAZlZgIJkAAJkAAJkAAJkAAJkAAJkAAJxJmA97LpOHeEzZEACZAACZAACZAACaQtAShKYTWKbcCAAXL06FFZsGBBwFXvtm3bfDtw8uRJmTVrltqQ8dJLL3W49S1QoIBveV5MLgKfffaZo0OwIAylTHcU+Pvk0KFDgpidWmCpXLNmTX2q9lOnTnWcx/qkcuXKrlUipiric5riZw2KfLCQPHDggFnEV6EcaRuJYOE2plAcTAC24jRc5aetOHX7bJjt8JgESIAESIAESIAESIAESIAESIAEEk2AitNE3wG2TwIkQAIkQAIkQAIJIpAnTx5BjEMd53DLli0BJSrc+sKSzk927dolY8eOVRtirVavXj2gSIVF64UXXuhXnNcSSODPP/8UO/7tlVdeGVWPZs+eLefOnQuUveqqqwTWq6Zs2LDBPJXcuXPLu+++60hLzYmXIs/Hx3x9AAAgAElEQVRW+IWKVYo+2GWQ5qdktPOHaiMRLGxXu/r/NRzmiIO8bt06R1Yv3o5MImIrThHTFveeQgIkQAIkQAIkQAIkQAIkQAIkQALJSoCK02S9M+wXCZAACZAACZAACcSZABRn2Hr06CFnz56VZcuWBRSpblZ1Zvdg1bdixQq1Pffcc5IzZ04VA/Ff//qXUqZCmUZJHgJbt25V99jsUdGiRc3TsI9ty1U3pZqtpC1UqJC0bds27DaizWgrDKHcD2VVa5cpWLCgwOW1l9j5Q7WRCBa2cjcSBeaaNWsEinZT3O6xeV0f24rTcMvp8tyTAAmQAAmQAAmQAAmQAAmQAAmQQLwJUHEab+JsjwRIgARIgARIgATSAYELLrhA6tSpo7Znn31WDh8+LPPnz1eK1Hnz5smOHTt8R3H8+HGZOXOm2pCxZMmS0qRJE6VEbdiwoeTLl8+3PC+mLYGff/45qIFixYoFpYVKgHvn6dOnO7LZ1pmIlbtz505HHtsi1XExhie2wtDum1tTdplQyj47v18biWJh9zHUmEwutmIYimcoh8MRKk7DocQ8JEACJEACJEACJEACJEACJEACyUSAitNkuhvsCwmQAAmQAAmQAAkkKYH8+fPLbbfdpjZ0ETEtv/jiC7UtXLhQTpw44dvzX375Rd566y21QfGCGJjaGhWKJihqKfEjcObMmaDGolFmI1ZuKGtExLXEZko0baH8wYMHVQxSXRc+S1WrVtWnjj2UdpEq7mBpvWrVKkc9fkrGSNtIBItIx+QYvIvr4goVKiiLcjuf23mk/N3qYBoJkAAJkAAJkAAJkAAJkAAJkAAJxJMAZ6jiSZttkQAJkAAJkAAJkEAGIQBXn9gefvhhgRJuyZIlAUXq6tWrBZZ1XoJ4mMuXL1fboEGDVMzDG264IaBIveKKK7yKMj1GBBDj0paVK1dK48aN7WTPc7hv/uCDDxzXs2XLJpUrV3ak4SRr1qzyxx9/BNJhqRqpoDw+J2a8TcTn/eSTT1yrsq0skcnPGhTXf/jhBzl58qSjPr8y0bQRbxbff/990Jj8lMGOwbsoTiMpaypOvT4bdns8JwESIAESIAESIAESIAESIAESIIFEEgieMUlkb9g2CZAACZAACZAACZBAuiMARVCDBg3khRdeECjf9u/fLxMnTpROnTpJ8eLFQ47n999/l2nTpkm3bt2kdOnSUqZMGenevbtyAYtrlNgTyJMnT1ClCxYsCErzSvj111+lTZs2gti2plSpUkUpSc00HJcoUcKRhDifkSpPH3zwQYfSFFbK/fv3d9RrntguZgsXLiyXXXaZmSXo2C6DDLCO9hI7fzhtxJuFrdy96KKL5JprrvEakiMdLrrtmKyRKE537doVqA+WwRdeeGHgnAckQAIkQAIkQAIkQAIkQAIkQAIkkIwEqDhNxrvCPpEACZAACZAACZBAOiZwySWXSLt27WTcuHECxQks3l555RUV4zR79uwhR4b4m2+88YZSzBUoUEBq164tiLMKJZWtqAtZGTO4EqhRo0aQe2RYDW/atMk1v5mIeKU33XSTurdmOo69lGp169a1swbi3wZdcEkYO3asYDPliSeeEChqvWTFihWOS14ufc1MtpIRSny4qfaSaNqINwt7TGAWrgLTHh84eN1jmxEs0aFg1xJuOZ2fexIgARIgARJIVgLwnILFfua2du3aZO0u+0UCJEACJEACJBAhASpOIwTG7CRAAiRAAiRAAiRAApERqFixojz66KMyZ84c+e2332TevHny+OOPK5euduxLu2bEZ4RC7+mnn5ZatWopZaydh+eRE7j44ovl2muvdRSEK1xYDnspT3EvJk2apJSVdhxQXZGXcqx58+Y6S2APxefMmTMD524Hhw4dki5dusi9997ruNyyZUsZOHCgI80+sS1a0f9QYisZvcaj64mmjXiziHRMemzY2xa1OXLkkEqVKplZPI/hUhluubWEYqnzcU8CJEACJEACyU5g8eLFsnXr1sCGRWVly5ZN9m6zfyRAAiRAAiRAAmESYIzTMEExGwmQAAmQAAmQAAmQQOoJIM5ho0aN1PbSSy/Jvn37lCJ17ty5Mn/+fIeFmltrp0+fdiTv2bPHcc6T8AnAtTKseU3Zu3evUlAjligUq7C4BOPNmzfL5MmTBde1VKhQQTZs2KBP1d4rHmirVq2UpaKpxMO9vOWWW5S73Tp16ggsQuFCGMrS9evXC/IOHTpUDh486GjjX//6l3z88cdBFrOOTCJBnyV8vu666y6l+M2VK5dcd911Dpe1x48fD3s8ui3TohJpodpAnniywJgQt9WUSBSY5v1CHdWqVQvJXbdll42kXV0H9yRAAiRAAiSQjATshUVwgQ9X+JTkJTB+/HhBCAItuGf4TUIhARIgARIgATcCVJy6UWEaCZAACZAACZAACZBAXAggJmT79u3VlpKSomJYfvHFF4INq/lPnTrl2w9YtVGiI3D99dfLc889pxSXpgvkI0eOyCeffKI2r5qhvOzcubNyyazzwKUtFK1e8vrrr0v9+vXl5MmTgSx//vmnsiZGAqyP8+XL55jUCmT8+6BevXoqHi4U8KEEdcGK1hTE3sUGWbhwoXlJxec1LSRxMZSyL9I2dIPxYoGYw+a9DWdMuo/Yp0b5aU4q47Nx5ZVXmlXzmARIgARIgATSLQHzOw6DgFcUSvISQPiABx54QMwFmAgjQsVp8t4z9owESIAEEk2ArnoTfQfYPgmQAAmQAAmQAAmQgCIAxVnlypWVG1+488WqcLj3hZtfuPt1E7gOpURPoG/fvopxwYIFw6oECstnnnlGudi1Y3k1bNhQKT+9KqpZs6Z8+umnkjVrVtcsUJyblgBmJlhxQMmLz0W49xz98ZIsWbJI9erVHZdtJSHigPrFUEXhSNvQDcaLhT0mKHr9lNu6f9hv375dDhw4YCaFVCSbmc22MV4KCZAACZAACWQEAvDEAde8pnh53DDz8DhxBNasWeNQmqInvGeJux9smQRIgATSAwEqTtPDXWIfSYAESIAESIAESCATEsiePbs0adJEsCL8+++/l927dwcpqkJZBGZCbBEPuXHjxrJ69WoVRxRuyy64INgpTenSpaV3797Khe6AAQOU8nPBggWOtm666SbHudsJ7ud3330X9mRVzpw5lXtduO6FkhfKzHBl5MiRAgtVN4GbYdRtiqnoQ3o4bvcibcNsLx4s7DFBgRkqrrDuo10W6eH+vx09etQRKzfccrpt7kmABEiABEggWQlccsklsmPHDsd22223JWt32S8XDxp410X4AQoJkAAJkAAJeBEInhXxysl0EiABEiABEiABEiABEkgggWLFikm5cuXkyy+/DPSC8aQCKFJ1ULx4cXn77bdVHXCPDBfIcNlbqFAhgTvlokWLOuqHJSIUoFqgjGvWrJk+9d1fffXVsmzZMuWKeerUqbJq1SoVjxRudXGP0ZdLL71UKT2hjIUCPRrJmzev+qzAcnLjxo0qVuvFF18sJUqUcLUknTJlSsTNRNqG3UBas4hmTLqPmASOdiIYsWptF8G6Xu5JgARIgARIID0TgOcMvEtQ0g8B27UyFsdF+36ZfkbNnpIACZAACaSGABWnqaHHsiRAAiRAAiRAAiRAAiSQwQhAGR3KQhAKT7jW1VKjRg2lYNXnofZQtNapU0dtofKm5jpc8l5xxRVqS009fmVT20a8WPiNgddIgARIgARIgARIIKMSsBWnod5zMyoHjosESIAESCB8AnTVGz4r5iQBEiABEiABEiABEiABEhCRd955x8GhXbt2jnOekAAJkAAJkAAJkAAJkECiCRw+fFi2bNni6Abjmzpw8IQESIAESMCFAC1OXaAwiQRIgARIgARIgARIIHMRQAzVEydOxGXQsDDMkSNHXNvTK+tnzZolL730kmOc06ZNk3z58jnS/E4+++wz5V5X54Fb3W7duulT7knAl0A8/9e2bt2aptbG5kDxf63/z8x0HpMACZAACWQcAvC28eeffzoGhPjr+A6wBSEIdu3aFUg+//zzg76TUBdCUHzzzTeye/du2b9/vyAMAEIkwJtH69atJTVhKaAwXL58ufz888+BDa70CxYsqNpo0KCBNG3aNKL3wMCArIPjx4/L/PnzBTHS9+3bp8aSO3duufzyywWx5TEWhEwIV86ePSvbtm1zZC9TpoyDNcIxzJs3T9asWSM4RoiH7t27C77/z507p8ouXbrUUQdOEKfWVKbiXZaue4MwMYEESIAEMjUBKk4z9e3n4EmABEiABEiABEiABDAJVrduXfntt9/iAuOqq64SxAiNV3tly5ZVMT4xuJMnT8rXX3/tGOeOHTvCnjDDBNZ9993nKD9gwIBUTeo5KuNJhiYQ7/81uFGOV6xV8/8sQ99EDo4ESIAEMjGB999/Xzp16uQgsHfvXilSpIgjDScff/yxI2+1atVk5cqVKh/eAQcOHCjjx4+Xo0ePBpXVCYgZPmzYMOncubNOCmuPRUqDBw8WxDr3+x5EfHsofvv06SP9+vWTbNmyhVW/mQmx6gcNGiSzZ8+WM2fOmJccx1Ci3nXXXWrcUNyGksWLFwsUu1oKFCggBw8eVKfHjh1T4xs+fLijTYSBwEJIvGtrxakub+5btGhhniqla8mSJR1pPCEBEiABEsjcBOiqN3Pff46eBEiABEiABEiABDI9gc2bN8dNiQnYWHUfL6Up2qtVq1bgHrtZln7wwQeB634HP/74o9x4443KgkDnq1SpkmNSUKdzTwJuBOL9v+Y3WezWv9Skmf9nqamHZUmABEiABJKXAKwpTSlRooSr0hR57LzaK8HYsWPlyiuvlBEjRvgqTVEHlKr33HOPjB492mzW8xhWn7fffrtcc801MnnyZF+lqa4EVq9QsjZq1CjImlbncdtDiXnbbbcpy9gZM2Y4FJhu+X///Xd54403pGrVqgKlaCjx4rd69WopX7688qBiK2rBGMppP6Wp3W6hQoWESlObCs9JgARIgARoccrPAAmQAAmQAAmQAAmQQKYmgFXvCxcujBsDuGDr1atX3NqDizQthQsX1oeB/auvvipwh/bcc88pF8KBC38fwL3be++9pyaoTp8+HbhcvHhxmTlzpsD1HIUEwiEQz/81TNDCwtrNCiicvkaax/w/i7Qs85MACZAACaQPArYyzy9W5rfffusYFCxOH374YRk5cqQjPZyTp59+Wjp27Cg5c+b0zA7XwLCk9HqnhSvaU6dOCbw/uAmUmX379pWhQ4e6XXakgUPbtm1l586djvRwTuCSuGHDhgI+UKJ6ic0aStE5c+bIrbfeKlAQ2wJ3yXBv/NZbb9mXfM9r1qzpe50XSYAESIAEMicBKk4z533nqEmABEiABEiABEiABP4mkD9/fqlXr16m4AEL0fbt24tpZQqrPLg60xYQV1xxheTKlUt+/fVX5bpsw4YNQWwQGwoxpagsCkLDBB8Cmel/zQcDL5EACZAACaRDAlA6rl271tFzbUXqSBRRCsp169Y5kl955RXZtGlTIA3vUHfccYeKewqLR4RxQDiF//znP7Jnz55APhwcOnRI5s6dK7fccosjXZ9gYRtiiJpKU8QTvfvuu5VnELiuhdtfKFexIA4eIOBe1x4PXPc+//zzyn2vrtveL1q0SBo3bhxkYYp3TPShSpUqUrFiRfUOuWTJEtGbaR0KK1f07bvvvpOsWbPaTahzW/EMhS/qNxfxISMUpni/wII+uANG+1gUCMG77EsvvaSO9Z8HHnhAufLV59WrV9eH3JMACZAACZBAgAAVpwEUPCABEiABEiABEiABEiCBjE9g1KhRahIL8UpNgYUe3Jvp+FvmNfMYk2Vvvvmmmugz03lMAiRAAiRAAiRAAhmVwJo1a4Jc2XopTt3yaqUpwibgXQxubm2vHYgBunHjRoF1KpScpuzatcs8dRxD2fnFF18E0uDhAdaZqMcUWJ1CwYntpptuEigREWdVC1wDf/nll9KkSROd5NgjnitcAZtKUChoEa+1Z8+ecsEF/5tmLleunDRt2lSVnzBhgrKYNStbv369zJo1SylDzXQcox17vC+//HJAaXrZZZfJv//9b2WxCiUtFvxpgcthbBDEmbWlf//+UqxYMTuZ5yRAAiRAAiTgIMAYpw4cPCEBEiABEiABEiABEiCBjE0Aq/Hnz5+vVvqbE1yhRl26dGnBxBcm5mCVSiEBEiABEiABEiCBzELAdh0LpaeXtaKdVzNC7NFVq1ZJu3btgpSmOg8UjgMGDNCngT2UiW6CcAuwFDVl2LBhQUpT8zqOs2XLpqwx7XdB29pVl0M7UPbCilMLFJjff/+99O7d26E01df1vkOHDspNsT7Xe4R8cBM3fnC/nyVLFqWg/eGHH6Rbt26C+OKm0tSuy7ZahVUqlaY2JZ6TAAmQAAm4EaDi1I0K00iABEiABEiABEiABEggAxOA4hMWBnDV9uijjyqLAEzUwRIBAuUq3LrBhTHiasFy4qefflJufjMwFg6NBEiABEiABEiABFwJ2Mq8ChUqeMYctRV2qBAx7qdPnx5WmIPrrrsuqA9wR+smqNNUqkKhCwVnOALL1EKFCjmywqWvmzz++OOCOKhaEG8Vis9wwzbAVTHaMwWuet3EZq3zjBkzRoWX8Iv1qvNib9fjF5PWLMdjEiABEiABEvifDwWyIAESIAESIAESIAESIAESyFQESpUqJZjIMgWxo2CFQCEBEiABEiABEiABEvgvAVsZ6uWmF7lthR3S3nnnHcF7VzjiZkXp5e1j0qRJjirPnTsniF8frowYMUJOnDgRyA4rTls++eQTpbA00z/66COBBW24AsvWqlWrOlwKI0yEm9iskadr165y7733umV3TYOFrB1+gopTV1RMJAESIAEScCFAxakLFCaRAAmQAAmQAAmQAAmQQGYlQKVpZr3zHDcJkAAJkAAJkIAbgcOHDyvPG+Y1L8WpW17EBb3lllvM4r7Hv/zyS9B1L8UpFIS2wNXvCy+8YCe7nrdt29Y13UwcMmSIeSp169aV5s2bO9LCObEVx3C/a0tKSorYlqgY+2uvvWZn9T2HC2E7TqzXPfOtiBdJgARIgAQyJQG66s2Ut52DJgESIAESIAESIAESIAESIAESIAESIAESIAESCEVgxYoVQVm8lHBuee+5556g8n4JCI9gCmJ7IoSCm+TIkSMoGYrO9u3bq1ALQRcjTNi4cWOQIrN79+4R1vLf7LYi080t8KZNm+To0aOO+rt06SJZs2Z1pIU6sa1WwbBGjRqhivE6CZAACZAACSgCtDjlB4EESIAESIAESIAESIAESIAESIAESIAESIAESIAEXAh8+umnjtTSpUt7uqm185YvX14aNWrkKB/qZMaMGY4sKO8V1/ORRx6R9evXCywsTfnwww8FG2Kror+w9oTl5pVXXilly5ZVmx3f1CyPY1h/IrapKVBgLly4UL755hszOazjJUuWOPKVK1fOcY4Tm1/x4sXloYceCsoXKsGup3Hjxp4MQ9XF6yRAAiRAApmPABWnme+ec8QkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJhEJg2bZojV6tWrQQWjG5i573rrrs887qVP3TokCxatMhx6eabb3acmyc1a9aU5cuXywMPPKAUpVB2mnLkyBEV69OO94k81apVE7j1bdGihVkkcAzl6Oeffx44x8GZM2fkzTffdKRFe9KwYcOgoja/li1bilvM16CCRsKxY8dk/vz5RoqIH0NHRp6QAAmQAAmQgIi4f8sTDQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlkYgLbt2+XAwcOOAh4uel1y/vPf/7TUTbUiZurX6/2dF0XX3yxTJgwQbnm7dGjhxQrVkxf8t2vWrVKoJjs2rWra76pU6e6pscqsXLlyo6qTp8+LWvXrnWkhRq7I/PfJ4iR+tdffzkuRXofHIV5QgIk8P/snQW4VNX39xfdJSUppZQ0KEiI0khIioDxExAJaVBCQkJRBBQMkFJKVEREaRAklRAQkG6Qkm5Q3ue7/555T8+Ze+/cOzP3u57nPHN27/05l+HMXnutRQIkEO8I0OI03j1yLpgESIAESIAESIAESIAESIAESIAESIAESIAESMAfgd9++81SxUkJZ46rmSBBAoFFaCBiHi9FihRSrFgxT10UL15cxo8fL+PGjRPMZcmSJcryEvd379517GPixInSqFEjqV27tqHO7t27Dem0adPK5MmTDXnRSZiVotu2bbPM01zHy3hmhogD++ijj3ppyjokQAIkQAIkoAhQcco/BBIgARIgARIgARIgARIgARIgARIgARIgARIgARIwETArQzNnzix58uQx1fq/pFlhh1ii6dKls63rlGkeD+50EycObPsWCtvy5cura9CgQXLt2jVZtmyZLFiwQOAKF+57zQKXvWbF6YEDBwzVEBO1adOmhryYTJjXDhe9dnFQ/Y1p7qdMmTKSKFEif81YTgIkQAIkQAI+AnTV60PBGxIgARIgARIgARIgARIgARIgARIgARIgARIgARL4PwJmZaibBWQgdZ34xkQf5r5Tp06tLEqnTJkix48fly5dupirWCw9ESsVdfWSPn16fTLG781rL1u2bEDxYbUJmftxshDW6vOTBEiABEiABMwEAjuyZG7NNAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlEGIF79+4J4oDqxUlxGkhdfX/6+8OHD8v58+f1WWKn9Dt06JBcuHDBVy9r1qySK1cuX9rtBkrUsWPHyvr16wWxQDUxW2TCahWXXjJkyKBPer7HmhD/VROMVapUKS3p+zQrPJ1Y+xrY3Jw8eVJw6SUq/ejb854ESIAESCD+EaDFafx75lwxCZAACZAACZAACZAACZAACZAACZAACZAACZCAC4Fdu3bJjRs3DDXsFJmosHPnTkvdQBV2ZsUh+rXro2/fvip2KuKn4hozZoxhjv4SUIiaFa2ZMmWyNEuaNKkhD4rdQOXmzZtSrVo1w3yHDRtm6ebixYuyf/9+Q77d2g0VbBJ2DJ2emU1zZpEACZAACZCAIkDFKf8QSIAESIAESIAESIAESIAESCACCTRr1kzy58/vu1577bUIXCWXRAIkQAIkQALBIWCOlYlRoKi0E7PCLlmyZFKiRAm7qo555j4QTzVv3ryW+v/++68hL2HCwLZ37969K+axXn75ZUOfSJiVq4h5GqjytHPnzrJjxw5f34jX+tZbb/nS2o15PsiPiuLU/MxgjZs7d25tGH6SAAmQAAmQgCcCgf3P6qlLViIBEiABEiABEiABEiABEiABEohrAnBVB3d+2nX69Om4nhLHJwESIAESIIGwIWBW5hUoUEAeeOAB2/mb65YsWVLMFpu2DXWZZqWfk+LQrDg1j63r0vZ29uzZBne2Dz74oIqBaq5cpUoVc5YsWLDAkueUgZiquPTSp08fARuzmNeQLVs2yZkzp7ma3/SmTZsMdexcAhsqMEECJEACJEACNgQY49QGCrNIgARIgARIgARIgARIgARIwI3AxIkT5dq1a74qZcuWFbsNRl8F3pAACZAACZAACYQVAbMyz0mRiUUFUtcOQiAxUjNmzGjoYt26dUpB+corrxjy7RJQmnbo0MFQ1KlTJ0mSJIkhD4l69erJpEmTDPlQfMIKtn79+oZ8feLvv/8W1Js6dao+Wxo0aCBDhgwx5GmJ6PLT+jFbxIIrhQRIgARIgAQCJUDFaaDEWJ8ESIAESIAESIAESIAESCDeE8DG36lTp3wcevXqRcWpjwZvSIAESIAESCC8CeBw1O7duw2LcIqVibqIh6oXNyWrvp52jxipiAeqF6fxoCD9/PPPfVVhgdqmTRv5448/BEpQKDYTJUrkK4fycNGiRTJt2jT57rvvfPm4adq0qfTr18+QpyUaNmyo3OXqlZq3b9+WJk2aKHe7lStXFlh0pkuXTqAsxfio+/7778v58+e1btRnzZo15euvvxa46rUT/RgoD5Sf1qfZu8by5culVatWyso1TZo08sQTT0jx4sW16vwkARIgARIgAVsC9v9b2VZlJgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlENoEtW7bIP//8Y1ikkzIPdc3uc52UnoYOdQmz4hBFTvFUy5cvL6VLl5atW7fqehAZO3asuuAi+OGHHxbESMUhr+PHj1uUsmhYo0YNmTlzprjFSB0/frxUrVpVbty44RsLMVIHDhyo0gkSJJAMGTLIhQsXfOXmmyeffFLmzZsniPtqJ0eOHJGzZ88aipxYGyrZJDAXswJ61qxZgguyatUqm1bMIgESIAESIAEjAcY4NfJgigRIgARIgARIgARIgARIgAQiggA2KY8dO+a7zO72ImKRXAQJkAAJkAAJBIGAWZEJV7Z2sTkxtLkulHeIhxqImPtwi6eKfkeMGGHrXhdld+7cURawUBLu27fPokiEorR169ZKmekvDiuUt7BSdap3//59R6Vp8uTJZfjw4bJs2TJJmTKlIw7z2qGMdVIaO3byX8HTTz/tWAXrLlOmjGM5C0iABEiABEhAI0DFqUaCnyRAAiRAAiRAAiRAAiRAAiQQQQSyZs0quXLl8l2ZMmWKoNVxKSRAAiRAAiQQPAJmZR7cu0IRaCfmulD6QfkXiPz666+G6v4sVmvVqiXz589XbnkNDV0SmFPz5s2VS93p06dLqlSpXGr//yKMtXnzZvE3J61F6tSplXtcuO6FG2C7+KlaXXya+T3yyCPK/a++jtf7cePGCSxc7aRIkSKCuVFIgARIgARIwB8Buur1R4jlJEACJEACJEACJEACJEACJEACJEACJEACJEAC8YbAN99843mtgdR16hRKxkClTp06yqIUFqGwLoXy9cyZM3Lp0iXlOjhPnjxKsZovXz71Cde8xYoVC3QYVQ+eFx8AACAASURBVB/tNmzYIGvXrpVvv/1WuQlGPFG4xc2ePbvkyJFDcubMqZSWzzzzjKRIkcLzOKNGjRJcMSHp06eXlStXCtz/7tmzR7kqhoIYB8mcLIZjYlz2QQIkQAIkEFkEqDiNrOfJ1ZAACZAACZAACZAACZAACZAACZAACZAACZAACcQDAokTJ1ZWpLAkDbbAYrVy5crqCvZY0ekfLnmhLMZFIQESIAESIIGoEKDiNCrU2IYESIAESIAESIAESIAEIozAv//+K+vXr1dxrHbu3Cl//fWX3LhxQx566CG18ZQ/f36pUKGCVK1aNVor37Vrl3Itt3HjRmUVcfbsWeX6LkuWLFKwYEGBOzhYRKRNm9bzOMePH5dbt2756qOvdOnS+dK4OXTokHz//fdy8OBBZX2Azb9s2bIJrDGeffZZefjhhw31zQlYb5w7d86Xfe/ePd89bi5evCj79+/35SGWF6wvvMjvv/+u3NQdPnxYWUjAggNuddEeF55B3bp1JU2aNF6689W5e/euIPaYJthIxAarncQGQ7txkXf79m1ZunSp4nfgwAH1eeHCBcmYMaPiAAsR/F3ATWKgrg+dxmQ+CZAACZAACZAACZAACZAACZAACdgRsP/VbFeTeSRAAiRAAiRAAiRAAiRAAhFJ4Mcff5Ru3boppaJ5gVA4/vzzz77sSpUqyZAhQ+Tpp5/25Xm5WbdunXTt2lW2bNliWx3u1H755Rf5/PPPlYIQ8+nZs6dFAWrX+LnnnlPu47SyiRMnSrt27VQSMbm6dOliKNfqaZ99+vSRihUryuTJk5XyVsvXf06bNk26d++uzzLcoy0uTaDoW7x4sZa0fP7zzz9KSf3hhx8K2PgTuJlr0aKFvPrqq/LYY4/5q67KEeMLbvU0adiwoVIea2n9Z2ww1I+Heyh1v/rqKxX/DC71nGT27NnyxhtvSNGiRZUrv9q1aztVZT4JkAAJkAAJkAAJkAAJkAAJkAAJRItAwmi1ZmMSIAESIAESIAESIAESIIGwJXDnzh1p2bKl1K9f31ZparcwxLaqVq2aamdXbs6DJWunTp0EClcnpam5zdWrV2Xo0KFKUYZ4XVERKOUGDBgg5cuXNygPnfqC8rJMmTLK8tGpTkzlX7t2TR5//HHlWs+L0hTjXr9+XSlm0e7dd9+Nqam49hNMhrBohgIYf39uSlP9BGGtjHhu+HvSW9Lq6/CeBEiABEiABEiABEiABEiABEiABKJDgIrT6NBjWxIgARIgARIgARIgARIIUwI3b94UWCDCmi8qgnajR492bQpXsc8//7x88sknjvWSJUvm6H715MmTAqvJ7777zrG9XQGUarBuHT58uMCy06tAOfnCCy8oF8Je2wRaDy5+mzVr5qpEhptfN+nbt6+MGzfOrUq0y4LJEM8EfxewBraT5MmTC9wKOwn+nnr06OFUzHwSIAESIAESIAESIAESIAESIAESiDIBuuqNMjo2JAESIAESIAESIAESIIHwJfD6669bXMlCWQVlKtzBIt5o+vTplSXq3r17lfITMVD1AvepsOh84okn9Nm+e5R//fXXvjRuMEarVq2Ua1zETIX7VSjSTpw4oWKfTpgwQTCeJoh/2bp1a+XOtlSpUlq26ycUuvo+oIhEH4iRiXihUF7CcnblypWC+KJ6QczVESNGCFzo6gUK3DFjxviyBg0aJFeuXPGln3rqKWnQoIEvjXHsBC6IzS58EY8V7o9hgVmoUCHJkCGDQLGNWKdwXzxp0iQ1X31/UAxXr15dChcurM+OsftgMNQm99Zbb8nq1au1pPqsV6+echeNeKaIbQpraFiiIibtjBkzZNasWYb6eD4vvviieP2bMDRmggRIgARIgARIgARIgARIgARIgARcCNynkAAJkAAJkAAJkAAJkEA4EOjYseN9hEbULqQpgROYP3++j6HGMmXKlPeXLFni2Nk///xz/91337W0K1eunG2blStX3k+QIIGhfqJEie7PmDHDtr6WeevWrftt27Y1tMMc8+bNe//27dtaNcNnhQoVLPW1dTVv3vz+uXPnDPW1BNbUuHFjS9vcuXNrVRw/s2fPbmjXq1cvx7pawb179+6nSpXK0O6xxx67f+TIEa2K42efPn0M7bC+iRMnOtZHgZlLw4YNHeub62r88BmTDC9cuGD5u+jRo4fjvLQC/G1mzJjRwKB27dpaMT/DmAC/18P44XHqJBAHBPidEQfQOSQJkAAJkAAJ+CEQSf8/4zews/8jF00ri0iABEiABEiABEiABEiABMKTAGKOdu/e3TL5uXPnSs2aNS35WgYsRWFB2qRJEy1LfW7dulUuX75syEOic+fOhjiUSZIkka+++kpZm1oq6zLguvfzzz+Xxo0b63JFDh8+LF9++aUhz18ClrNwKZwpUybbqlgTLBlh6amXY8eOyfbt2/VZMXL/559/qlilWmeJEyeWmTNnKitYLc/pc+TIkVKiRAlD8YYNGwzpYCSiwxAWs2bB34s+Pin+LoYNG2auZknjbxNxb/WyZMkSZZmrz+M9CZAACZAACZAACZAACZAACZAACUSHQFi66l22bJksWrRI4LaLQgIkQAIkEDkEsFlep04dqVGjRuQsiishARIggRAjgPfoQ4cOGWbVtGlTqV27tiHPKQEXqfPmzRMoYCFwswu3t88884yvyYoVK2T37t2+NG7atm0rGMerwGXv8uXLDe5w33vvPdWPlz6yZcumXOtCOeom+L+nQ4cO8ttvvxmqHThwwKKoNFSIQmLTpk2GVnD/W6BAAUOeWwIukfUK3W3btrlVj3ZZdBleuHBBHnzwQcM8tmzZYkhjjBQpUhjynBKIPwsX01rcWihg9+/fr1wwO7VhPgmQAAmQAAmQAAmQAAmQAAmQAAkEQiDsFKdQmrqdhA9k8axLAiRAAiQQegQQP27p0qVUnobeo+GMSIAEIoQArDnN0q9fP3OWYzpHjhzy+OOPi97acdWqVQbF6WeffWZp3759e0ueWwasRBEzFPEtNYGSDNfDDz+sZTl+Dhw4UBDb1IsgVqhZzp8/b86KdhoWmPqYpHXr1g2oz8yZMxvqIw5oMCW6DG/cuGGZHmKW6gXWvcePH5dcuXLps23vU6dOLXPmzJFr1675yhEPlUICJEACJEACJEACJEACJEACJEACMUUg7BSnOCFPIQESIAESiGwC+K6n1WlkP2OujgRIIG4IwELvl19+MQwORWipUqUMef4SXbp0kXz58vmqZciQwXePMVauXOlL46Z8+fJRst5s3ry5QXGKvjB/f4pTzAcWrl4FVpEJEiQwuJD9+++/vTb3XK9v376CK6qya9euqDYNuF1MMLx586Zl3LJly8rEiRMN+S1atJCFCxdKunTpDPl2CbOraLs6zCMBEiABEiABEiABEiABEiABEiCBqBIIO8Up3fNG9VGzHQmQAAmEDwF+14fPs+JMSYAEwovAvn375OLFi4ZJ613sGgpcElB04bITjAEXrXpp2bKlPun5vmrVqpa6mzdvljZt2ljy9RkFCxYUxA/1KqibJk0ag1vgK1eueG0e9HpwTYv4sN9//33Qx9IGiAmGdv+f2z3T9evXS+nSpVUMXbhzfuCBB7Rp8JMESIAESIAESIAESIAESIAESIAEYpWA992EWJ2W98EeffRRqVKlivcGrEkCJEACJBByBGA9tHPnzpCbFydEAiRAApFG4Pfff7cs6bHHHrPkRSdj69atlub58+e35HnJgDIT7nb1Ll/PnTvntyniZgYqiRIlMjSB5WxsCpSjJ06ckMOHD8uRI0cMFyxNg+E62G19wWIIa+H69evLggULDMMj7i7cOXfs2FG5goaCFb/zKlasKHDRSyEBEiABEiABEiABEiABEiABEiCB2CAQ9opT/Jj++OOPY4MVxyABEiABEggSgU6dOlFxGiS27JYESIAE9ATs3M+a42bq60fl3k7B5yV+pdNYWbJkUUpErdxszarl6z9TpEihT4bkPeKTrl69Wn7++Wd1bdmyRe7evRsycw0mw2+//VbgcvfHH3+0rBcKZFig4hoxYoQkTZpUKlWqJHXq1FFX0aJFLW2YQQIkQAIkQAIkQAIkQAIkQAIkQAIxRSBhTHXEfkiABEiABEiABEiABEiABEKbgNlNL2Yb04pTuzFy584dZTD6+Kno5NatW377SpYsmd86cVkBMWCLFy8uNWvWlHfeeUc2btzoV2maJEkSyZo1a6xNO5gMoQydO3eu9OzZU1kUuy0KCmbw6t27t8DbEBSnU6dOFeRTSIAESIAESIAESIAESIAESIAESCCmCVBxGtNE2R8JkAAJkAAJkAAJkAAJhCiBq1evWmYW0/Ek9W51MRjih6ZLl84yrtcMs2vehx56yG/TBAkS+K0TFxXg/rdt27ZSrVo12bt3r98pwNq2du3aMnz4cDl69Kglrmww1xnMvrFwKE9HjRql1jVy5Eh54oknJGFC/z9Pd+/eLa+88opSop49e9YvQ1YgARIgARIgARIgARIgARIgARIggUAIhL2r3kAWy7okQAIkQAIkQAIkQAIkEJ8J5MiRw7J8OwtRS6UAMszWpffu3RMoP6Ni2frvv//K6dOnDaPny5fPkA6nxEcffSSTJ0+2TBlucRHTs3Tp0lKsWDHJmzevuqLCzNJ5iGdkypRJ+vTpoy78nSxbtky5Ll61apUcOHDAcfb79++XZ555RlAvVapUjvVYQAIkQAIkQAIkQAIkQAIkQAIkQAKBEKDiNBBarEsCJEACJEACJEACJEACYUzgkUcescz++PHjUr58eUt+VDMefvhhS1NYS0ZFCXjmzBmB4lUv4ao43bVrl7z55pv6pah7KA379u0r6dOnt5TFtwz8jbRs2VJdWDv+NpcsWSKLFy+WhQsXys2bNw1INm/eLAMGDJAxY8YY8pkgARIgARIgARIgARIgARIgARIggagS8O8LKao9sx0JkAAJkAAJkAAJkAAJkEBIEbBTah47dizgOUIRCks/7dqyZYuvjwIFCvjutRvUj4rYubMNV8Xp9OnTLfFZhw0bJnBT61VpanaDHBWm4dQmV65cyrXxt99+KydPnpQePXpYpr9mzRpLHjNIgARIgARIgARIgARIgARIgARIIKoEaHEaVXJsRwIkQAIkQAIkQAIkQAJhRiBPnjwqtuSdO3d8M9++fbvv3utNly5d5IcffvBVR8xJzQUtYpAifqV+jE2bNkmTJk189b3efPPNN5aq4ao4/eOPPwxrQdxXWJoGImYlN2KmhpP8/fffcvjwYd+UkydPrmKV+jJcbjJkyCAffPCBXLt2TSZOnOirCa74W8PfHIUESIAESIAESIAESIAESIAESIAEokuAFqfRJcj2JEACJEACJEACJEACJBAmBBIlSiRmi1BY8124cMHzCq5evarcp+ob1KlTx5dMmDChmJWbkyZNsrhZ9TVwuIGLXrPiFK6GYYUYjmJWnFaoUEHAyqvcunVLoIAOZ4G74nLlyvmuxx9/XBDHNhBp3ry5oTqUpmaFsqECEyRAAiRAAiRAAiRAAiRAAiRAAiQQAAHvv9QD6JRVScBMAC60EidO7Hht2LDB3ITpOCCAU/xwFWd3jR8/PmgzgvVBkiRJbP8+8ubNG7Rx2TEJkAAJkAAJxEcCnTt3NiwbcSOnTp1qyHNLTJs2TW7fvu2rgne8GjVq+NK4ee655wxp/F8PV7WByLJly+TcuXOGJv379w9I2WhoHMeJ69evG2YAa8tA5IsvvghIwR1I37FVt3Tp0obnB9fDu3fvDmh4vSUzGqZMmVL4vhgQQlYmARIgARIgARIIMQLwzKLfNw3UK0mILYfTIQESIIGwJ0DFadg/wvBYwK+//ir//POP7YWT9iVLlgyPhUT4LNeuXSuXL1+2vYoVKxa01cN6AlYldn8jZcqUCdq47JgESIAESIAE4iOBdu3aiTnW6ZgxY+TIkSN+cRw8eNDiXhaWk3A7q5fevXtLtmzZ9FkqluepU6cMeU4JKMfefPNNQ3H+/PmlZcuWhry4TCRIkMAwvFmhZygUsbzv/vbbb+rdx1zPLr1582bp2bOnXVFY5aVOnVoKFSpkmHOgh/NWr15taI/fEbCkppAACZAACZAACZBAuBKABw79nligHjnCdd2cNwmQAAmEKgEqTkP1yUTYvLAx5CRQyKVIkcKpWPr06SOPPfaY7bVnzx7HdiwInIDTc4JyO5gKTKdxsQI8ewoJkAAJkAAJkEDMEcBp9nfeecfQ4cmTJ6Vy5cri9m61b98+adq0qZgtJ80WrOg4VapUMnToUMMYhw4dkvLlywvctboJrFnbtm0rO3bsMFTr16+fOolvyIzDRJYsWQyjb9y40dXtrPmgIJTIZuWwoUMRQQxTxPOsXr26hTvq6i1/zW1DNV27dm3D1KZMmSIffvihWquhwCaxcOFCVVdf9MQTT+iTvCcBEiABEiABEiABEiABEiABEiCBaBGg4jRa+NjYKwFYnDqJP8UYYlvBItF8bdu2TfLkyePULfMDJIANUycrkCJFiggsBIIl0fn7CNac2C8JkAAJkAAJRDIBuANDfEm9nDhxQilPe/XqJYh7evz4cTl79qz8/PPPMmTIEGUxifcvvXTo0EHMMSe18v/9739i9liBPitVqqSsT9EvPF1ocu3aNfnyyy/VvMxufREz9cUXX9SqhsRn7ty5DfPAQTAoOAcPHiwTJkyQefPmGcpbtGhhsYwcNWqUUkZDSaxZFsDa4MCBA4rRo48+Ku3bt/dxMsenhQXwkiVLlIUCYs+GgwwaNEiyZ8/um+rdu3elW7duUqtWLfW+j78DsyA+7Ouvvy6NGjUSxHrVBM8A7pspJEACJEACJEACJEACJEACJEACJBBTBBLHVEfshwScCJw+fVqOHTvmVOxqUXjmzBlHt3ElSpSQQGNDOU6CBRKXyksni1O4XStbtiyfDgmQAAmQAAmQQBAIzJ49W6pWrWp4Tzt//rwg5rkXKVeunIwdO9axKjxWTJ48WWrWrCmXLl3y1cO9ZmkJd7dQfkFZhjiodgLLzh9//DGkrE0xT1hOzp8/3zBlKINxQaAIhKJPE/CCku/tt9/WstTn3LlzBRfeazNmzCh4d4by1CxQdn/66aeSI0cOgbIRAotUzAMcq1WrJogLG+qSNm1a+eijj5TCWD9XzF2bP9YIJTGUwfgdgb9Ls+DvCwr29OnTm4uYJgESIAESIAESIAESIAESIAESIIEoE6DFaZTRsaFXArAUdROztYO+rpNCDXX8Warq++G9fwJurN2ekf+e3WscPnzYdjMMrQoXLhxUS1f3mbGUBEiABEiABCKbQN68eQXxIvH/baBSp04dpexLmjSpa1MoC1etWiUPPvigbT0o/o4ePeqoNH3ooYeUMi0qc7QdMAYzX3rpJcmaNWtAPb711lvy1FNP2baBJSU8gJiVpmAMK82vvvpKMmfOrJTd5g7AMZwESuAuXbo4Thkc8Le5detW2/dEuIJGbNQqVao49sECEiABEiABEiABEiABEiABEiABEogKASpOo0KNbQIi4KaQw4nzQoUKOfbnZgUZTGWe44QiuMDtOQVTSe32jIM5bgQ/Si6NBEiABEiABDwTQNgDuN8dMWKEJ8s9/N8Mi0rEmsyVK5enceAl5M8//1TuWJMkSeKpTYoUKQRKRrQrXry4pzaxXQlzRFzTQN5JEV92+fLl8vHHHwveg90ErBo2bKieD9z/oi0EboAffvhht6ZhUYa4ptOmTVMWx14nDMtaKKwRbxduoikkQAIkQAIkQAIkQAIkQAIkQAIkENME6Ko3pomyPwsBN8VYmTJlBG62nMRNmRfIJpVT/8z/PwKIqbV582ZbHClTphTE1wqW8BkHiyz7JQESIAESIAFvBGDR2LdvX0Fs0xUrVsjKlStV3HPEN4VFJZR0uAoWLCilS5f21qmpFtypjhkzRrno/emnn2TRokVy6NAhQViG27dvK0tKWFNijGeeeUbFCoVVoVdZv36916qO9S5cuOBY5lQAxTPGhjvZvXv3qriweHfKmTOnlCpVyrYZ3n07duyo3Ph+9913snv3btm1a5digdifUEgjVAFiombKlMnSByyF4dEFCmxYZMJCFW3sLFkD4RJIXcuk/ssIlCGUoK1atVKumH/55RcVOgKxdhH79ubNm4pj/vz5BTFucSGGbFT/Bp3mzHwSIAESIAESIAESIAESIAESIAES0BOg4lRPg/cxTgBuw9xc9bopP93aYvPtkUceidZ84Q7t+PHjgk1BxNpCTKls2bKpy5/buWgN/F9jrO/UqVNqow1rwfheBXNHHDBsTiEmGGJ/IRZUVGO+wpoDMaTsBJt+moWDvhzzx2Yn5oGNTmzsuSnB9W31926K06hanMb1s9Wvj/ckQAIkQAIkEC4EYOGIeJm4giVQxL7yyivqCtYYsd0v3n+gQMUViOC9s1OnToE08dVNly6dPPvss+ryZYbpDd4zI2UtYfoIOG0SIAESIAESIAESIAESIAESIAEdASpOdTB4G/ME9u/fr5SSTj2bFWNz5sxRVgeoD4UcFJp2AkXd9OnTfUWJEiVSp9V9GQ436HPq1KmyZMkSWbt2rUDBZhZt0xCn3+EezYsyEhYG77//vrkrlX755ZelUqVK6h7Kxu+//15GjRolv//+uzpJjwKcsK9cubJte2TCIvTHH39U816zZo3s3LlT0JdZoHzFqXxYKLzwwgu2VgrmNkh7VV5evHhRZsyYoRhiDnfv3vV1B26wdGjZsqV6FnbKVl/l/27u3bunLCXM+UgHaukarGdrNzfmkQAJkAAJkAAJkAAJkAAJkAAJkAAJxD8C2MvBHsrhw4flyJEjcvr0abX3gsPsuBCfvW7dupImTZqA4Ny5c8dQ33ygH4fmsWcGTxUYGwfpEUO+QIEC0qBBA0Fcebi09yKxOZa/+ezYsUMWL16sYt6DJfa/cufOrTjWqlVLihYt6q8LSzmMJPT7fTA2wKEzvcDzCfbnDh48qIwawA6H2nAQDge6YiIsAkILYL8PRhMwPMCeHbyEFCtWTMqXL6+fDu9JgARIgARCjAAVpyH2QCJtOm5uerFWveL03LlzSuHnhQEUsnDtpUnhwoVdFadQqr377rvy2WefKQtNrZ3dJ5SBCxYsUBf6/fbbb6VIkSJ2VX15y5Ytk8mTJ/vS+pu2bduq5Pbt26V169ZK6akvh9LXyeUYlKNQ9I4cOVLFctK3s7vHOnHhJf7NN99Uit/Ro0crN2d29bU8N8WpZhU8ceJE5cLPyTIV3JYuXaquTz/9VGbNmqVcqmlj2H3+8ccfPuWxuRxMvChfg/1szfNimgRIgARIIHIJ+HtvicmVww3t9evXY7JL174wXjBd77sOzkISiCUC/DccS6A5DAmQAAnEMwJwiw/3+ojPvW7dOr+rx3sXDrS/+uqrhn0vp4Zw+Q+Fq1727NmjQhhgDwax6LGf5WRcMHz4cKVgxPyqVaum78ZyH5tjWQb/LwPKUex1YV1QYDpJz549BSG+2rdvL23atPHs5ey5556TDRs2+LrFfla7du1UGmGqunTpYij3Vfzvpk+fPlKxYkW1z4cwEoHK119/Le+9955s2bLFsSmUp927d5f//e9/jnVYQAIkQAIkEHcEqDiNO/bxYmQ3hZx2Gk8D4ebSV6vj9Kkp9+zKEf8J1pcnT560K3bNgwtbKHfnzp0rOOnmJE7rhBVmyZIlZfny5dK4cWNbd7g4PWcXw+vKlStKOYwTcFERnCD85ptvlGUtrFRhieokTvNH/eLFi6u5z5s3z6m5JR+bVlWrVlXWpHaxubQGbuPqlepaffNnbDxb85hMkwAJkAAJRC6B2Dz5DVf4sFiILcF4iIdJIYFIJsB/w5H8dLk2EiABEogbArD0xP6GmxLMPDMcjsPhelzvvPOOOthuruMlDeUprB/h5cyfIF46YoF369ZNcIDeq/Wp1m9sjQULTyg2vfJEPSigp02bpngWKlRIm3JAnzBMeOutt5RRBRTh/gQKcihtoTCvWbOmv+qqHPt4UNBCcepPYEiA8BUwQPjiiy/8VWc5CZAACZBALBNIGMvjcbh4RiAQxVh0Tog7KdngEhcvjlFRmmqPCi+8eJnBC5CTOK0TSke4HYHLXydLTTulL17McbotqkpT/Tz/+usv6devnz7LcH/z5k01R0PmfwkodF9//XUJRGmq9QPXKP5Ozrk9c6dnqvUfW89WG4+fJEACJEACJEACJEACJEACJEACJEAC8YcAwgs1a9bMVcmHMENu0rdvXxk3bpxbFduyo0ePqnBIXpSm+g7Gjh0rCBkViMTWWAj5hFBWXpWm+jWsX79eeWvDZ6ACpWnXrl0FlrlelKZa/9gPhCEG3Oz6ExgvQMntRWmq7+urr75SzwtWuBQSIAESIIHQIUCL09B5FhE3k9u3b8u2bdsc12VWjDkpHx070BWY+0IRXJT07t1bVyvqt4hHAOXj+PHjLZ3AVQriFtjJAw88IPXq1ZMbN27YFas8u7l37NjR4tLXsQMPBVDA4iUsYULrWQlYu+DHgJ3gJXHFihV2RZ7yEJcVfwOwurUTt2dup1DW+oitZ6uNx08SIAESIIH4QWDVqlWxttDUqVP7DR8Qk5PBeBQSiHQC/Dcc6U+Y6yMBEiCB2CUA603E39QLYmUOGTJEeSeD9WOGDBlUCCLE50Q8y0mTJinPX/o2UNrhUD/CQXkVhHpCSCsIrEehlIMSDzFN06dPr2KCQgH50UcfWdzdfvnll9K8eXN55plnPA0XG2PBoAGWuwi3pBfwQyguWHdi7wixWzdu3KgueE87f/68rzoO/iOeK6xBA3GhCwtcvQIaym6sGcYOcJGMPbG1a9fKypUrLR5hzp49q1wKYx/KTbCPB69oZkH/VapUUX8vjzzyiNrrw/pgaXr58mVVffbs2QKPdRQSV7tGYQAAIABJREFUIAESIIHQIUDFaeg8i4ibCWJ6mgPO6xdpVhgipmXevHlVFcRc+Omnn/TVffd40cTLll7wsqMXnNjCC66TIHbmU089pWJ9IUj87t27lYIQClInmTJlirz//vuSIkUKQxW4GMbpNTtB7FN/YuaAF9zp06c7NsNLMl5+8QmLULxI46Vx0aJFgjijdoLncOLECcmdO7el2E15aaksosbEC51TbA1zG8ThwGUWWODCFbKd4JnkyZPHrkhi89naToCZJEACJEACEUvgySefjNi1cWEkEB8I8N9wfHjKXCMJkAAJxA4BWCbCPaxesH8Di0JzPFLsE2E/CxeUgG+88YaKcam1xZ4RFHOBKE41pSkUtdjvqVChgtad+syVK5ey3uzcubMyGhgzZoyhvFevXirkFPa//Emwx8L6X3zxRYvSFHt72APDWvQCRSMElp5PP/202rPTyqF4RbxT8PQqeqUpFMoff/yxmMNKQTENgwNYGMM9r15gjOCmOMWeIvYMzQLvdZ988okkS5bMV6S5/T18+LAKi6UZnDjt5/ka8oYESIAESCBWCfj/3zNWp8PBIomAm0IOlo/lypUzLBcuMzTBiTknxSlePPAC6CQ45YeTXk7yxBNPKEUeArHrBYpA9O3klhYn23B6rG7duvpm4rZOQ0VdAi+FadOmlVu3bglinGqCl7SBAwdqScvnoEGDlOVr0qRJLWVz5syRFi1aWPK1DLPCV8v3Mn/8KEDQevSfNWtW1RSnAHGS8u233xZYpjqJ08vs5s2b1UupXTvz34ZWJ7afrTYuP0kgpgjgJCt+pCHWoPZjMKb6Zj8kQAIkQAIkQAIkQAKhTQCWS/3795fPP/88tCfK2ZEACaiD3vq9DiggZ86caVGa2qEaOXKkLFmyRGBQoMmGDRtU/Est7eUTh9Zh8eoWwztRokQqpin2lz799FNft4hZOmHCBOnUqZMvz+0mmGMh1iusOfXy/PPPy4wZM2w9o2n1sP8EbxJQnsLNryYwHgDfWrVqaVmePhErFYzsvLGhA+TPmjVL/VbX75XBuAPPskSJErbjII6t2aACHuvc2EPJDrfDiPe6YMEC236ZSQIkQAIkEHcErH47424uHDnCCLjFr4Q7kzRp0jiuWP+CYq7k5sIVdTt06CAXL140N1PpypUrq5cus9IUhXB1gpe2jBkz2rZFpp37L7e5ah3hhCBenOH2Fqfj8NKFl74DBw4IXnI1wQsxYkvYSbVq1WTw4MFipzRF/Ro1atg1U3nJkyeXzJkz25a7PSc0qFOnjnK3C9cymtIU+XBD3KdPH8XMtuP/MvGyDqWzWdy4OT3j2H625jkzTQLRIQALdPzQgjW8W8zk6IzBtiRAAiRAAiRAAiRAAqFHAJZEsAaDm0YcPqWQAAmEPgF4F9MLvBrA85dXwaF9vWiWhfo8f/ewrHRTmurbQ3ln3s/CHtK1a9f01RzvgzUWFIoffPCBYVxYeyLuq5MCU18Ze1nLly8X7GvpRa8k1uc73WfLlk19D/sbE9ah2HsyC/bv7ASWo/CMphd4iXNTmmp1YeAABavTPp9Wj58kQAIkQAKxT4CK09hnHm9GdFOMmd3TmqE4KfOgZETcAyfBiy1caNhJjhw5ZO7cua5xAxDnAFanTqLFH9CXu60T9YoUKSKYF5SMOCUHhaOT4DSgneAlClZqbgLXIE7i5A4GsSIOHTrk1EyxhgUulMpOAncmThaiaAP3NnaKbDdudn8fcfFsndbMfBIIhAD+jTVu3Fjgksft32kgfbIuCZAACZAACZAACZBAeBBAHDuElunRo4cvnl14zJyzJIH4TQAer7CXol1m72P+6JgPr7uFsrLrC/tfsFD3Kjiwbw5ZhT0fvdWrU1/BHAuWoThQrxeEwTIrefXl5nsc4ocLXb3AIxw8OnkVeHfDnp8XQTxas+hjrerLYCRhnoebJzl9W9wjpBaU1hQSIAESIIHQIkDFaWg9j4iZDRRl+/fvd1yPk0UhGsAi0+kk16OPPur6omOO6aCfAGJMmF9c9eXavVsds+UkLEfxMu0kOJEIJfDDDz/sVMWQD6Uv3PWaL7hccQt8f/DgQUFcCydx4m0+QalvDzc0cP+rj8WgL9ffwyrVTcwuS1DXSXGaIEECW0VsbD9bt/WwjAS8EIBbpwEDBqjDE04uwL30wzokQAIkQAIkQAIkQALhRwC/0XDIFK4kzUqD8FsNZ0wC8Y9A37591cFXHH7FhcMPgciuXbsCqW6pW7JkScmZM6cl3y2jXr16lmJ8F/mTYI61cOFCw/DY88HB4kAFLm31Ai9OW7du1Wc53mfIkEHatm3rWG4uePDBBwXz1Av2Ku0Ern31gu98O2MAfR3zPf7WKCRAAiRAAqFFgDFOQ+t5RMxsoJCzU5ZpC3R7iXBT5jkpANHvqVOn5JtvvtGGMHziJJvXE1xu8zZ06qL8Qz28ZCFWa+rUqc3NHNPmFzO7ilCqYq14+f3zzz/ll19+UVa2ZqWuvq0TNyflJdq2atVK8ufPr+/G8d7NihaNoITVy8mTJ+XEiRP6LN89xjT3FxfP1jch3pBAFAjMnj1bevfuLfhbp5AACZAACZAACZAACcQfAnCLCZeZcE15+/bt+LNwrpQESEARgNctuG518obmFRMO4gcqCA2TJUsWQTxlTbwoToM51po1a7SpqE+4O06bNq0hz0sClvtm2bdvnyclJQwRzPtS5r70adRFeDF9iB39vVYXnK9evaol1adbGC1DRV0iV65cAvfFTlatuqq8JQESIAESiCUCRm1GLA3KYSKfgJtCDj787WKMalTc2ropXBFM3eweQ+sTFrBe3YA49YG+zKf93ObasGFD5ZJJm0NUPuEaGIpRuFbRriNHjghi5AQiTopTJ5fI6Lt79+6eh3CzSoUrFLy46yVQ5XhcPFv9fHlPAl4J4MRrly5dZN26dV6bsB4JkAAJkAAJkAAJkEAEEMAB3JkzZwo8HeHgJ4UESCByCUA5isPgiG+JPRr9BUvTmFCAOe3juFHFYXzEUtYrTp08uun7CdZYODyyY8cO/VCSJ08eZQRgyPSYwP7SjRs3fLXB3YsgvmmgAvfFerEzsrBTSj/00EP6Zp7v4bI3Jv5uPA/IiiRAAiRAAq4EqDh1xcPCqBJwUyiWKlXKNc6omzLP7WVu8eLFjtOFlSbc3UZX8ubNa+jCbZ1wyxRVWbZsmUyZMkXmz58vbpakXvpHnItChQrZVnVSYOK0G04qepWjR486VoWbYrMlrRs3O+V4XDxbxwWxgARsCJw7d0655Z00aZJytW1ThVkkQAIkQAIkQAIkQAIRSmDLli3q8Nz69esjdIVcFgnEbwKIT7p69WpBXE1c+Dcf6IH2QAnCAjEqYm5np9wz92tuYy53SpvbmceCe1vsx+kF+11FihTRZ0X5HkYSXgQGHMEQ83oxBhSgURG08+p6OCr9sw0JkAAJkEBgBKg4DYwXa3skEKhiTN+tU1u4yXB6uYKV6IoVK/TdBOUecR80wQnDzZs3a0nDJxSFiGsQqGAd3bp1k48//jjQpo71y5Yta1FcovKhQ4ccT7MFOne3eLZ6Ztok3ZTjZsVpXDxbbZ78JAF/BPD3iX+vgwcPlkuXLtlWx78n/LDm6VFbPMwkARIgARIgARIggbAlAKuufv36ydSpUy3KASwqSZIk0qhRI/n666/Ddo2cOAnEdwIrV66Ujh07yt69ez2jwL99hCA6c+aM5zbmiojLGRUxhz7666+//HYTrLGc4oL6nZDHCl4NJNy8pHkcyraaneI0OhantoMwkwRIgARIIE4IJIyTUTloRBOA9aHby6FZMaaH4abMK1OmjCRMaP8nixdYc1wBfb8xcY8XX31Mhd27d8v169dtu3700UcFweQDEShd6tatG5DSFO5G8ALfuXNnx6GcrHSdFNToqHTp0o79mQvgrsRNEdqsWTNDE5w2dFI4J02aVGCRrJe4eLb68XlPAk4Eli9friyzcdjBTmmKeL2wGofFNA5+UEiABEiABEiABEiABCKDACzNxo4dq1xiTp482VZpWqdOHfnjjz9k4MCBkbForoIE4hkB7HW0bdtWqlWr5klpihBFtWvXluHDhwv2xVq0aGEgZvbEZSi0SURVmYmQT3qBFzJ/EqyxEPM5mOI1jnSg7L3O2U4pHdXf/jDOoJAACZAACYQOAVqchs6ziJiZuCnksEgnRR7K3Nq6tXOL2VC4cGHXMb2ChyJUf0otqnO1Gw+xMapXr+76Mp4qVSoV9B4cYMUJRXKBAgVUd61atbLrVuU5cXNTdgai9N24caOK7WE3Abx816hRw1AEReiVK1cMeVoCimk9Y+THxbPV5sNPErAjgAMevXr1knnz5tkVS+rUqZXlQY8ePSx/z7YNmEkCJEACJEACJEACJBA2BJYuXSpdu3aVPXv22M4ZoUpGjx4t9erVU+WId0ghARIIPwIfffSR4GCEWeD2tWrVqurAebFixQQhnXBlzpzZXDVaaa/WlOZBjh8/bsgyW6AaCv9LBGssO6Vt48aN5fnnn7ebRsB5UbXuDHgghwYIc2UW7O8hzmygYn5ugbZnfRIgARIggZglQMVpzPJkb36Un4h/kC9fPkdObso8JwUgOnNTrtWsWVOdBnYcNIoFbopTN6tau+FGjhzpqDSF8hE/vJs3by4pU6a0ay5QXjqJ01zc5h+I4vTbb791Glq5pYIVqV7cnrHdXOPi2erny3sS0AjAwvydd96RUaNGid3JVpxibdmypbz33nuSPXt2rRk/SYAESIAESIAESIAEIoAAXDL27NlTeRSxWw6sjPr37y/du3cX828gu/rMIwESCF0COPDw5ptvWibYp08f6du3r6RPn95SFtMZUQ31YlbAeVGcBmssu7GheG7atGlM44qT/nBQxizHjh2j4tQMhWkSIAESCEMCVJyG4UML9Sm7KcbKlSvnOn03ZZ6dUk3rDCe6nMTJva9Tfa/5but0m6u5f7hRmTZtmjlbpXPkyCGIp+F2Wg1KU1jA2QmCy9spQRGX8ffff7drovJQ7kUw95kzZzpWNbumQUW3Z2ynHI+LZ+u4IBbEWwKzZ8+W3r17y8mTJ20ZwAJ83LhxUqFCBdtyZpIACZAACZAACZAACYQnAbiaHDFihDrM6nR4rnXr1oLDsAilQiEBEgh/AtOnTxezFeawYcPU4Qivq7tx44bXqrb1Tp8+bZvvlgk34ubQWXZWkeY+gjVWxowZVaxnzEuTffv2abdh/+mkOI3KwswK76j0wTYkQAIkQAIxR8A+YGTM9c+e4hkB+OTfsmWL46rdFIp4kdq6dattWygQcTmJ24neYChO8QK8c+dO2+nAKhQxTr3KpEmTxCnuA04zuilNMcbnn3/uOJSdIhKVEWvn5s2bju3WrVvnWKYvgCtS80u5Vg43NU8//bSW9H26KU7t/j5i+9n6JsobEhBR30mVKlVSlqR2SlPEscG/QfxdU2nKPxkSIAESIAESIAESiBwCiG+IQ6IFCxZUXkfslKY4GLx+/Xr58ssvqTSNnEfPlZCA2jPRY4DLWViaBiKwPNQLvlMCkdWrVwdSXdXdvHmzJeZy5cqV/fYTrLGSJEmiwkzpJxBVxSn2DLE+/eW0l6YfL5j3dorTX375JeAhjxw5IlG1+g14MDYgARIgARLwRICKU0+YWMkrAbgzcTtV56TIQ/9Q5plP9Gnj+rNUdXJhi/bLli0TfxaUUPi+9NJLKnYo4oear6lTp2pTUZ9Q8DoFbi9durQkSpTIUN8p8e+//8r48eOditWcHAtF5NSpUzJnzhzHKk683axl0RmsXP3JokWLZMqUKY7VBg0aZOGA57tjxw7bNnB1g00Js8T2szWPz3T8JHDu3Dlp37694LvH7iABfgDCDRt+9LVt21aCcUAjfpLnqkmABEiABEiABEgg7gngMDAOz8GSFL+5zJI1a1YV+xC/q8qXL28uZpoESCDMCWB/Si84JBvIbz7sfWzatEnfRcD3CxcuDLgNLN/1gnAyTz75pD7L9j6YY5kVt/BctnjxYtt5uGV+8skn6vc5fqPjQr/YU4tLSZ06teXQDPbo/v7774CmhfBcFBIgARIggdAiQMVpaD2PsJ+NP4WcnUWhtmi3tnYKNa0dPt1cIkFR56acxKk/KD5wSnj79u2WC+4yELxeL4FaTerb6u+hnMHJMjtJliyZ4FSjk8CVCiw6EXfRSZwUzm7zR19QNr/77rtO3cqnn36q4pc6VahRo4Z07NjRUgz3wHoXLfoKZcuWFbzUmyW2n615fKbjFwEcsvjwww+VpffEiRNtf4jVqlVLHQDAjxu3f6PxixxXSwIkEG4EmjVrJvnz5/ddr732WrgtgfMlARIggRgncPbsWWnXrp3gdyssSc2Cw3OIc4rDc6+88ort7xdzG6ZJgATCj4B5nyV58uQBLeKLL76QCxcuBNTGXBkhmRYsWGDOdkzDkOGHH34wlBcrVkzgDcyfBHMsu3imw4cP9zclQ/mVK1cErpL1Ur9+fUmbNq0+K07uX3jhBcO4UJrDs5xXgZJ18uTJXquzHgmQAAmQQCwRoOI0lkDHl2HcFHL58uUTxDdwEre2eIlzcy0LRYabvPXWWzJ27FjRu1eCwnTVqlXqJLFTjFH0OWTIEItyxE3J66YcNs/RzToXc92wYYO5iUqvWLFCnnrqKdm7d69tOTKhhITlrJ24sdbqww1Nhw4d5JtvvhG4mPnzzz9VLNaGDRsqpaiepdYGn7AchSWqnRLUbVwnbrH9bPVr4X38IrB8+XIpUaKEdOvWTS5dumRZPBQM8+fPV6djCxUqZClnBgmQAAmEEwG4H8f7lXZFJbZVOK2XcyUBEiABNwI43DlmzBh1eA4b3nZWTHXq1FFekkaNGhUSm/Vu62EZCZBA9AiY91Kwl+Hkdcw8ElzJ4oBFTAjCI7ntG2ljYH+md+/eYnYHHMg8gjUW9nqqVKmiTVV9rl27VpYuXWrIc0sgzrTZlW3Lli3dmsRaWb9+/SRTpkyG8fD/CfbQvAj2K708Yy99sQ4JkAAJkEDMEaDiNOZYsicRFefPCYST21it/oEDB7RbyyeUd6lSpVIKTMQUnDVrlqHOQw89pBQehkxdAnEP4FYTp9FgvQrlCPqD8tHuJLHWtEuXLtK5c2ct6fuMigLQ11h346YMRrXq1atLq1atZN68ecol79ChQ6VUqVIqf8+ePbqerLdQVNudvrt69arnF7jPPvtMmjdvLuBbpEgR+d///mc5wWge+eOPP5acOXOas1U6Kgrn2H62thNnZkQTgNKgUaNGAkvp3bt3W9YK9zv4oYYTvA0aNLCUM4MESIAE4gMBWOHD0l67ohK/KT5w4hpJgATCjwA274sXLy5QGsCFpFkQww5WX3Bl6c8Tkrkt0yRAAuFJwKw4hcvuN99803UxUFrifQn7OGaLVTR0Onzu1in2yfA79eLFi47V4MmsWrVqgnBKesH3FfaTvEowxxo8eLDlcP2zzz4rM2bMcJ0eFIo40G92QQz3w7A4DQWBFyoYXOjlzJkzypWw2x4YDuwgZBg8ulFIgARIgARCj0Di0JsSZxSuBPBiCMWCkzhZFGr1/cUAwEso3HNAzC+xyBs3bpxUrVrV9nSwNsadO3eUWyUt7faJF0xYqZrFzb0uXKDkzZvX3MQxDQvcpEmTCuZlJ3hJhJLYrCjW6iLGht1paJQXLVpUq2b4xOlHpzZQEEHJHFWBixK3U39RVTjH1rON6rrZLjwJ4DvrnXfeEVgN2P2IhdU0vgfwIy179uzhuUjOmgRIgARiiAA2hPRx/nr16mWxHoihodgNCZAACcQKgYMHDyplqdm1pTZ4mjRppH///uoALn6zUUiABOIPgRYtWqgQLnorU/xuPHz4sAwcOFAeffRRFfMU5cibO3euCv+kP4hboEAB0RsI4DtnyZIlSrGKvR58x3gRHPYvXLiwUiDC7W2ePHkkUaJEgr2ddevWCQ68m0NAYa/ogw8+UPW8jKHVCdZYMFrA96ne3S4MCbCHtHr1aqlXr57a58PBefw2B8dt27bJe++9J2ajAey7YY8MDEJFXn31VbUnqZ8r9jih0O7UqZPAkAShtHLlyqX+JmBxO3XqVNEfRISxgv7vJ1TWxnmQAAmQQHwlQIvT+Prkg7DuLVu2uLou8ac4zZo1q6dZ4TQXXhrNgsDwAwYMMGcHnIayBK42EJMiUHezTjFFnSaBNbdp08ap2DX/iSeeUC5xnSo5xV50U17ihwAseqMiiI2GFz8nwUsjfijYCV4e3WKZxtaztZsb8yKTwOzZs5XFAGKr2ClNEXMXP0KnT59OpWlk/glwVSRAAiRAAiRAAvGUAA6K4vceDpraKU3xG/DFF19UYVHeeOMNddA1nqLiskkg3hLA3g4UfWaBglTzYAZPW8mSJRNYpcMaVa/0atKkifJuhrjImsAYoHbt2oK8xo0ba9mOn/o4yrBghNUmFLY48I4L+yQY16w0RYfwBPbMM8849m0uiI2xMP9mzZqZh1bxQGF9CoUwQj9hbaVLl1ZxpPWKSDSEQjgUf6MnTpxY7YeZvb7hsDaUv/h7yJ07t/J8h78XeHPTK03h6a1jx44WNswgARIgARKIOwJUnMYd+4gb2U0hhxdDvPi4CU5oeRG8wNopNNEW1hDjx48XvLRERfDi+9NPPwmUKU6n19xcbfhTDtvNCbFEvZ40RHucdn733XdlzZo18sADD9h1qfJwklH/4q5VdHtONWvWVK5lnNau9aH/xEst4jfAvYhbu02bNumbGe69cIuNZ2uYFBMRSWDr1q0qrjEsoxHfzyw4OIC4Vvh3XqFCBXMx0yRAAiQQMQQQBgAxzLUL330UEiABEohkAlBazJw5Ux2eg9cRu8Nz+K25YcMGdYjW7WBnJHPi2kiABP6PAGJPwlLSTm7duqV+T+otUlEP+zWDBg2Sr776SmAZCa9oZsF3kRd57rnnlAIUykKzmMfVypMnT672Z3CwPRCJjbGwXzRnzhyB1xIngbv0e/fu2RbDC9Ty5culVq1atuVxnVm+fHk1P3iWcxK7WKbYh3MzQnDqi/kkQAIkQALBJWD93ze447H3CCbgplAsVqyY4AXOTZ5//nnlbgPuKdzEX6xUuMGA9Wsg8Q4QDxTuTWARWadOHbfhXeO4elEAmjuHteUff/yh4laYy/RpKIPhvgQKSJx8xsszTuM5ydmzZ+XHH3+0FDspThHMHm6GGzZsqNzMFCpUyNJWnwGFKU4l7t27V7p166Yvsr13+/vwyi3Yz9Z24syMCAJwsd2+fXvlHgeWpGbB4Q7EQd63b5+yArf7cWpuwzQJkAAJhDMBeL3AO4h24T2AQgIkQAKRSgC/DytVqiStW7c2uB3X1ovvxClTpqjDc/5+b2pt+EkCJBDZBLAHA0UdrDfNloTmleP3JPZS4F4WlpXaYf4JEyYoi1Rzfa9pxPfE4X4v8ZWh/ISFppf9GbvxY2MsGEG8//77yj1vmTJl7KZhyYOhQdu2bWX79u2OimxLozjKwOEbhDDDs/AnKVOmVGGDFi9eLLinkAAJkAAJhBaBqJnlhdYaOJsQIfDNN99EeyZQnuI6ceKE/Pnnn3Lx4kV12gwvV3iRSJEihRQvXtzvOKgDt0v79+8XvITABcZff/0lUJ7AlQpO/uHHMX4UV69e3TEeqN1A6C+mBXEcli5dqhSdsIiDpeihQ4eURSnceUDxjBcvsxtduGbxelpRm/Px48e1W8dPvPA3aNBAFi1apF780QauYWDhilN+eMHFKT9/ynD9ADh1iSu6EsxnG925sX3oEcBpVfzQxY/XS5cu2U4Qf8uIZ+zvsIBtY2aSAAmQAAmQAAmQAAmELAEcJoW7TShF//33X8s8oezo0qWLilnoTzFiacwMEiCBiCeAA7VwodqoUSP57rvv1F4NFGPYH8HeCA6gIcwLYqLaHULD4XQcfv/5558Fez2wFEUbJ0tWO6Bw74v9MXgV+/777+Xo0aPKYwgO0sPwAFeVKlXUPo1d+0DyYmsszBdccMAee3cbN26U06dPq9/s2KuDNzhcFStWVG6NA1UsIlZrdOXChQtR6gLzh8UxYuEiRBDimWKPE1bKOXLkUC57YRSBvym99zkYCuCikAAJkAAJhAYBKk5D4zlwFiYC2kuSKTvgJGIH4Hr99dcDbhsXDfDyhCsUBMrqunXrqisU5mOeQ7g9W/P8mQ4+gWXLlqnTtnYuqzF6/vz5ZfTo0eqQQPBnwxFIgARIgARIgARIgARii8Ddu3dVCBeE+4DrRzuBpyGEHPFiyWXXnnkkQALxhwBcd0dVqZUuXTpBDE9cURXsz0CpiSvYEltjYRy4t8UViQKF9tChQyNxaVwTCZAACcQLAlScxovHzEWSAAmQQPwhAGvtnj17qtO4dquGm+l+/fpJjx49lAW6XR3mkQAJkECkE4BSQe+1AhYVmls5rB1W+vDUoYk53hS8gsCzhyawBMApeq+yY8cO5RUEVhOwMIAlGLxswAsHPAEULVrUa1eqHvq4evWqrw28ZOhjTMHCAyEMNm/erEIkIAbZtGnT6BrNR4w3JBA5BODJp2vXrsplpd2qcAATh+dC5cCq3RyZRwIkQAIkQAIkQAIkQAIkEHcEqDiNO/YcmQRIgARIIAYJXL9+Xd555x0VJ+T27duWnnGitVWrVjJy5EjlVslSgRkkQAIkEI8IPPnkk7JhwwbfiuGmH+7fNIFSEbGfnWTy5MmCSxMoO/2FM4BydOrUqTJixAgVkkBra/7E4ReEBUBs6jZt2qi47uY65jTmCrdomsA157Bhw1Ry586dKi47XMLpZeLEiVSc6oHwngTCnMDBgwfVwTi4fbQTuEQcMGCA8kiCwxMUEiABEiABEiABEiABEiABErAjkNAuk3kkQAIkQAIkEE4EZs2apdysDR8+XOyUpohFpQehAAAgAElEQVQ7s27dOpk+fTqVpuH0YDlXEiCBiCEAhcZjjz0mbdu2dVWaagvesmWLvPrqq4J47nv27NGyA/5csGCBUsKalaYBd8QGJEACIUvg2rVrypsILNXtlKY4PPfiiy/K3r17pU+fPkKlacg+Sk6MBEiABEiABEiABEiABEKCABWnIfEYOAkSIAESIIGoENi6datUqlRJWZKePHnS0kWWLFlk0qRJ8uuvv0qFChUs5cwgARIgARIIPgFYfOK7GsrQQGX9+vVSunRpwWegsnr1amnevLncuXMn0KasTwIkEAYE4G58xowZ6vAcvI7YHZ4rV66csq7/4osvBDEKKSRAAiRAAiRAAiRAAiRAAiTgjwBd9fojxHISIAESIIGQI4C4e3DDCDeRcP1oliRJkkjnzp1l0KBBki5dOnMx0yRAAiRAAn4IwJXvmDFjfLXwfXrlyhVf+qmnnpIGDRr40ohNaic41FK1alX5+++/DcUZMmSQl156SVmDlixZUi5cuCAbN25U15o1a+T8+fO++jdv3lRjwXNAwYIFffluN5jr888/L7du3bJUS5s2rSDmKYUESCB8CeAgRpcuXRwPVWTNmlWFcHj55ZcFFqcUEiABEiABEiABEiABEiABEvBKgIpTr6RYjwRIgARIIM4J3Lt3Tz7++GMZPHiwXLp0yXY+iLM3duxYKVSokG05M0mABEiABPwTKFWqlODS5P333zcoThGDtFu3blqx7SesweAe06w0hSL1yy+/lFy5chnaValSRaXPnDkjTz/9tOzevdtXjj4Q73Tt2rW+PLcbHKy5ceOGr0q7du3k2WefleLFi0vOnDl9+bwhARIILwJnz55VbnkRL9np8BwUqgMHDhQckqCQAAmQAAmQAAmQAAmQAAmQQKAEqDgNlBjrkwAJkAAJxAmBZcuWqU16/Ua6fiL58+eX0aNHGyyg9OW8JwESIAESiF0CUF6uXLnSMCisQOFaM2FC54ghsBRbtWqVUp7Cza8msDhdsmSJ4ICMP9GUpunTp5cpU6ZIo0aN/DVhOQmQQAgTuHv3rowfP16GDBkily9ftp1pnTp1lKW8V8t0206YSQIkQAIkQAIkQAIkQAIkEO8JOO9YxHs0BEACJEACJBAKBA4dOqQ2vGvWrGmwPtLmljp1auWKbdeuXVSaalD4SQIkQAJxTADWph988IFhFpkyZZJx48a5Kk21BpkzZ5bly5dL8uTJtSz1+emnnxrS/hLfffcdlab+ILGcBEKcwNKlS5W1eI8ePWyVpg8//LAsWLBAFi5c6Nmdd4gvmdMjARIgARIgARIgARIgARKIQwJhb3H6yy+/SKdOneIQIYcmARIgARKILgF8l5vl+vXrSiE6atQouX37trlYxatq1aqVjBw5UrJnz24pD6UMKBD0go3/RYsW6bN4TwIk4JGA3feFx6asFosEYBm6Z88ew4hw95sxY0ZDnlsClqfNmjWT6dOn+6r9/PPPArftiRP7/xnTpEkTQSxWCgmQQHgSOHjwoEBZ+sMPP9guIE2aNDJgwADlkSRp0qS2dUI1k3sYofpkOK9wIBCJ74IPPPCAOlym51+4cGF9MsbuY3OsGJs0OyIBEiABEiCBWCbgf8chlifkbzhYFOkF7rv0Lrz0ZbwnARIgARIITwKIYVe5cmX5/fffbRfw0EMPCRSqjRs39mS5ZNtJLGYiXp9eYBFBIQESiBkCa9asiZmO2EuMEjBbhkJhCje9gQpiFeoVp1euXBG4bodLTjdJlCiRZQPSrT7LSIAEQocAYpdqMe3Pnz9vOzHEQR4zZoyyRLWtEEKZ+N4yyyeffGLOYpoESCCKBI4dOxbFlqHTDF6UOnfuHCsTis2xYmVBHIQESIAESIAEgkAg7Fz1pk2bNggY2CUJkAAJkEAoEYBidOvWrcrCoECBApapHT16VFkhFStWTG2gWyqEWEaSJElCbEacDglEDgG+G4bes4SXgB9//NEwsdKlSwtcr//5558BXalSpZKUKVMa+tqyZYshbZcoWrSoZMuWza6IeSRAAiFOADGQYZF54sQJGT58uOB7wCyIn1yiRAlp2rSpHD582FwcUmkoKSgkQALBIwDrcwoJkAAJkAAJkAAJxCSBsLM4zZUrl2H9cDER6i4aDRNmggRIgARIwELg1KlTcuHCBV++9l1fv359QWzT0aNHy4gRI+TatWu+OrjZvXu3Km/UqJGyQM2XL5+hPFQS+L9Kb21Qt25dyZMnT6hMj/MggbAiAPdsem8j2DinhBaBv//+W2AxphdYiRYpUkSfFeX7ixcv+m2LeKoUEiCB8CaQLFky6devn7z00kvSu3dvmT17tmVBc+fOlZ9++kmVv/nmm5aDFpYGcZABRbBZOnbsaM5imgRIwCMB87tghgwZPLZkNRIgARIgARIgARLwRiDsFKfmZbVo0UK58THnM00CJEACJBA+BGBV4OSyDJtmffv2VZtmffr0kVmzZok5Zui8efME7m979eql6tpZJsQljQQJEhiG79Chg9SrV8+QxwQJkIA3Avi+0CtOvbVirdgkAMVpMOXWrVvB7J59kwAJhBiBHDlyqPc/fP/DfTe8kugF3wlDhw6VadOmCWIpP/fcc/rikLyHK2IKCZBA1AjwXTBq3NiKBEiABEiABEjAOwHr0UfvbVmTBEiABEiABGKNALwLzJgxQ9atWydly5a1jAvXkHDnVrBgQVuLBEsDZpAACZAACQSFgNk7QEwPgu97CgmQQPwjULFiRdm0aZNMmDBBMmfObAFw/PhxwcHqKlWqyLZt2yzlzCABEiABEiABEiABEiABEiABLwTC3uLUyyJZhwRIgARIIHIIVKhQQX799VeZMmWK9O/fX86ePWtY3MmTJ6Vly5bKG8FHH30kiKtHIQESIAESiD0C6dKlswzWuHFjef755y35UclAHGwKCZBA/CQAt7evvvqqNG/eXAYPHqze9+7du2eAsWbNGilTpoyqB0tUuu424GGCBEiABEiABEiABEiABEjADwEqTv0AYjEJkAAJkEDoEcCmWdu2baVZs2YyZMgQGT9+vNy9e9cwUVimlitXTtUbNmyYrWWCoQETJEACJEACMUIAcZ3NkiJFCmnatKk5m2kSIAESiBKB9OnTy9ixY6V9+/bStWtXQRxlvSDO8meffSZz5syRt99+W1577TVJnJjbH3pGvCcBEiABEiABEiABEiABErAnQFe99lyYSwIkQAIkEAYEYNU0evRo2bFjh9SqVcsyY2yaTZw4UR555BGB9anZIsHSgBkkQAIkQALRJpAxY0ZJkiSJoZ99+/YZ0kyQAAmQQEwQKFy4sCxdulQQ7z5fvnyWLi9evCivv/66lCxZUlasWGEpZwYJkAAJkAAJkAAJkAAJkAAJmAlQcWomwjQJkAAJkEDYEShUqJAsXrxY5s+fL/nz57fM/9KlS8oaoUSJEhaLBEtlZpAACZAACUSLAJSmcJOpl6gqTuFNYPPmzYYr2DFU9fPmPQmQQHgQePbZZ2X37t0CLyOpUqWyTHrXrl1SvXp1Zfl+5MgRSzkzSIAESIAESIAESIAESIAESEAjQMWpRoKfJEACJEACYU+gQYMGgo2xESNGSOrUqS3rwYZazZo1pVGjRnLo0CFLOTNIgARIgARihkDlypUNHV2+fFkdcDFkekh88sknyu06XK/jQr/wJkAhARIgATOBZMmSSf/+/WXv3r0q3r25HOm5c+cKrFQHDhwoN27csKvCPBIgARIgARIgARIgARIggXhOgIrTeP4HYF7+xo0blbUWLLa0a/v27eZqTJMACZBAyBLAplnfvn3Vplnr1q0lQYIElrl+//33UqRIERkwYIBcv37dUs4MEiABEiCB6BGwi2c6fPjwgDq9cuWKsh7TN6pfv76kTZtWn8V7EiABEjAQyJEjh8ycOVPWrl0rpUuXNpQhcevWLRk6dKjAYwlioFJIgARIgARIgARIgARIgARIQE+AilM9Dd6rH5ewwtKu48ePS8GCBUmGBEiABMKOQPbs2WX69Omybt06KVu2rGX+t2/fFmzi4ztu9uzZlnJmkAAJkAAJ/H8C5kMod+7c+f+FNnePPfaYVKlSxVACJQZiEXoVeA84f/68oXrLli0NaSZIgARIwIlAxYoVZdOmTSrefebMmS3V8Fu3RYsW6rtq27ZtlnJmkAAJkAAJkAAJkAAJkAAJxE8CVJzGz+fuuOpff/3VUFa8eHFJnjy5IU+fmDZtmowePdp3LV++XF/MexIgARKIcwIVKlQQfLdNmjRJsmTJYpnPyZMnlTu3SpUqydatWy3lzCABEiABEhDL9ye8lPhzmTt48GCL1T/iEM6YMcMVKdxndujQQUaOHGmo9+STTwosTikkQAIk4JVAwoQJpV27doI4y127dpXEiRNbmq5Zs0bFZcb3jvmwhqUyM0iABEiABEiABEiABEiABCKeABWnEf+IA1ugWXFavnx5xw5gafDaa69Jz549fdeOHTsc67OABEiABOKKADbN2rRpozbNunfvLkmSJLFMBZapiJ/Xvn17OXfunKWcGSRAAiQQnwnkzp3bsPzffvtNqlevLlCOTpgwQebNm2coR+Kpp55S8Qb1BTdv3pQXXnhBKTLmz58vR48eVcXwAvD777/L1KlTlQLjs88+0zcTWIvNmjVLEiVKZMhnggRIgAS8EEifPr2MHTtW8Hu1Ro0aliY4CILvnUceeUTGjRsn9+7ds9RhBgmQAAmQAAmQAAmQAAmQQPwgQMVp/HjOnlZ56tQpgbsivTz++OP6pOEe7oywyaUXt/r6erwnARIggbggkC5dOmUhj02zWrVqWaaATbOJEyeqTbMPP/yQm2YWQswgARKIrwRq165tWfrPP/8sQ4YMUQfpoDy1EyhWmzVrZimCFwBYn+bJk0eg0EidOrWKRfjKK6/Inj17DPVx+AWu1+GCnUICJEAC0SFQuHBh5TIchz3y5ctn6erixYvSpUsXKVmypKxYscJSzgwSIAESIAESIAESIAESIIHIJ0DFaeQ/Y88rzJQpkxw7dsxwNW/e3LE9LA30ArdHpUuX1mfxngRIgARCkkChQoVk8eLFAmun/PnzW+Z46dIl6datm5QoUUKWLVtmKWcGCZAACcQ3Ai+99JJkzZo14GXDQnTOnDnSq1cvx7aXL192PKgCZSlCQdgddnHskAUkQAIk4IcADm7s3r1bhg0bJqlSpbLU3rVrl7Kqb9KkiRw5csRSzgwSIAESIAESIAESIAESIIHIJUDFaeQ+24BXljRpUsmVK5fhSpYsmWM/Zre+iIeaIkUKx/osIAESIIFQI9CgQQPBxtiIESOUtZN5fthQq1mzpjRq1EgOHTpkLmaaBEiABOINAbzjIa5pVLyLJEiQQN5//31ZvXq1csPrBVqaNGmkbdu2sn37duXy10sb1iEBEiCBQAjgt27//v1l7969Kt69XdvvvvtOYKU6cOBAQfxlCgmQAAmQAAmQAAmQAAmQQOQToOI08p9x0FZoVpw+9thjQRuLHZMACZBAsAhg06xv375q06x169aCDX6zfP/991KkSBEZMGCAXL9+3VzMNAmQAAmEHYH169fL/fv3fRe+5/wJ3Oqi3eHDh5XV/ueffy4zZ85UCtFvvvnGX3OpUqWKbNq0STZs2KC+dxEDFQqJbNmyKbeY9erVU25/4Zb39OnTgv7hEcWLzJ4927cWrIsuNr1QYx0SIAEQyJEjh/ouW7t2ra0HpVu3bsnQoUOlYMGCyoKe1EiABEiABEiABEiABEiABCKbQOLIXh5XFywCFy5ckP379xu6j4oFgqEDJkiABEggDgnAHSQ26zt27KhiW23evNkwG8R0Hj58uEybNk3ee+89R8sEQyMmSIAESCDCCCDeKBSouKIiOJxSvnx5dUWlPduQAAmQQLAIVKxYUR3umDx5srJEPXfunGGoEydOSIsWLeTjjz+Wjz76SB34MFRgggRIgARIgARIgARIgARIICIIUHEaEY8x+ovAyfy7d+8aOkqSJInB8gpuKv/55x9VB9YGZoFFgF6ZmjNnTs+ue69du6biVyFu6pkzZ+Ts2bOSNm1atSkHKy/EoLGLPWOegz597949ZRGhzytQoIBhTYhXg/iF27ZtU7FrnnnmGaU00bfhPQmQQPwiUKFCBYFF/dSpU6Vfv37q+0hP4OTJk9KqVSv55JNP1KYZYzvr6fCeBEiABEiABEiABMKXAA6HtGvXTpo1ayaDBw9WSlL8rtTLmjVrlNtx1EOMVK+W8fo+eE8CJBD6BOD94urVq76JPvDAA5IxY0ZfGvtjP/74o+DA7R9//CEIf4VDtilTpvTVcbrZsWOH8t5x9OhR5WXj33//ldy5c8tDDz2k4roXLVrUqakh/9SpUxaPSIhJj/20QOXKlStqP07fLnPmzJI+fXp9luN9TK1JP0AwngEORC9dulTtXx44cEB9wjgEzxbf5yVLllTPAOHI7LxR6efn5T4YXLyMyzokQAIkQALRI0DFafT4RUzrL7/8Ul5++WXDev766y958MEHVR5cUz7yyCM+xamh4n+J+vXrG7KhlMRLn5ts3bpV3n77bVm0aJHcuXPHsSpe+qCoGDJkiODFzYvA1RJcwGmCl6Dz58+rJF5+8SN37NixhnErV66sVecnCZBAPCaATbM2bdpI06ZN1ffO+PHjLYdL1q1bJ+XKlVP1YInq9bspHmPl0kkg5AmYwxAEc8I4EBabrr8x3qOPPhrMJYVl33zmYfnYOGkSCDoBKArwW7F9+/bStWtXddhWPyiUHBMmTJCvv/5avSt26NBBEifm9oqeEe9JINwJdO/eXb766ivfMhATGftIkJ07d8orr7yirNR9FURk4sSJjopTfG/gcO6IESMEhglO0rNnT3U4A98/+E2K36ZOAgt5xGDWCzwowTI+UHnrrbfUwWB9O3/x7YOxJv34MfkMYDCC54nD0divdBKEf3jjjTcEyutRo0ZJ7dq1nao65gebi+PALCABEiABEogxAnyzjzGU4d0RLD31kitXLp/SFPlbtmxxVZrq2+I+S5YsrkpTKDDxMvftt9+qeFTm9uY0Tr59+umn8sMPP6gXnUqVKpmrWNLmNWkxWH///XeBkhdWY2bR6pjzmSYBEoifBNKlSyejR4+WV199Vbp16yZLliwxgMAPIsTgQ2w/WCV06tSJm2YGQkyQQHgRgAvZ2JJSpUoJ3kliSzAeDqxRjAT4zI08mCIBEjASQBxmWCYhDjSUGWZlx8WLF1WIByhRP/zwQ6lWrZqxA6ZIgAQijsCCBQvUAVu3w//mRR88eFCee+45tbdmLrNLYw8Ov0FhwQrlaKFCheyqSevWrWXQoEGGfbXvvvtOxo0b56pwNXeG37U4CKIXfP+5heQK1pr0c3C6D/QZwDCkQYMGyjrYqU9z/q5du6ROnTpq7xIHqb1an8YlF/MamCYBEiABEog6AedjS1Hvky3DkIBZyWh+OTKX+1sirLCcBH3BtSUUDTjxFYhA2fn000972mg0zxlK0cWLF0uVKlVslaZ4CSpbtmwg02FdEiCBeEIAP1Tx/TF//nzJnz+/ZdWXLl1SitUSJUpYLBIslZlBAiRAAiRAAiRAAiQQVgQQOmb37t0q3r1dCBlssFevXl2aNGniaskUVovmZEmABCwEVq9eLc2bNzd4LrNUMmXAOhWH/6EMDVQQJgv7Z3bhstBX3rx51R6Xvl+4t4V3pEAE60I7vZi90unLgrkm/Th294E+A7hUfv755x2VpsmTJ3dVMiNET48ePeymYsmLSy6WyTCDBEiABEggWgRocRotfJHR+NatW7J9+3bDYsyWl/DxD6srCF6m3nvvPUP91157Tbny1TLLlCmj3Ro+f/nlF6lRo4blJROu4/BjFOPAHQbcZuBFT7v0J/kQi/Wll15SLz2IIeEkZtdrUNJiDMQz0AsUpohVkSNHjijFgdD3xXsSIIHIJoBTqrVq1ZIxY8aojTPEZ9YLNtRq1qypvms++OADyZcvn76Y9yRAAiFOYNWqVbE2w9SpU4v5OySYg2M8ipUAn7mVCXNIgATsCSRLlky5eMRv0T59+sisWbMsFWHptXDhQundu7e8+eabji47LQ2ZQQIkEPIE4AkNCjjsoZkF4aWgoDMLDv9XrVpV/v77b0NRhgwZ1L4W9s6wD4YYm3CLiwtxlLUwU2h08+ZNZS2J/bGCBQsa+kHixRdfFCgT9QLvboGEotK7JEY/iRIlkhdeeEHfpe8+NtbkG8x0E5VnABfEZj716tVTB5/BHmG9sOeIfUhYi86YMcPy/Q6PAuAMDy5OEpdcnObEfBIgARIggegRuB9O0rFjR5go+i6kKdEjsGHDBh9Pje2qVascO50zZ46l/smTJx3rawWnTp26/+CDDxrapkqV6v6oUaPu3717V6tm+fzyyy8NbbQ5zps3z1JXy8BYWj3tM2XKlL683Llz3//kk0/uY+1XrlzRmvGTBEggjgiE43c7vvdat259P0GCBL7vFu37Bp/JkiW7379///vXrl27nzdvXkOdBQsWxBFpDksC4U8gHL8vwp86V0ACJEACwSMQrt/ra9euvV+6dGnDO57+XTBnzpz3Z8+efX/nzp2WOsGjyZ5JIPIJxNZ3RosWLQz/dvV7Svi33q5du/s//fTT/ePHj9tC//fff+8//fTThj7QrmrVqvePHTtm2waZp0+fvl+kSBFLu4oVK9q2uXz58v0UKVIY6uP7B+N7EezHZcyY0dC+bt26tk1ja03a4NF9BhcuXLD8Xu/Ro4fWvePnkiVLLExq167tWD+2uThOhAUkQAIkEIcEYuv/59hYIv6/pqve6CmdI6K12aUtTpY5WYxiwWZLTlhqZs+e3ZXFvXv3lDsTveuP3LlzC9xYIFZM4sTOxs845dalSxdL/4hp4CTmNaHejRs3lPuNrl27ClwpdejQQRDXKk2aNE7dMJ8ESIAEHAnge2/69OnKMt7OzTes24cPH65OBetPDDt2yAISIAESIAESIAESIIGwIVCxYkXZtGmTTJw4UTJnzmyZ94kTJ5R1WtOmTS1lzCABEgg/AthTgqRPn15gXY5/+3Xr1pWcOXPaLgaxSVeuXGkog8XqihUrJFeuXIZ8fSJr1qwCjxjwzKYXWJwuWbJEn6XuYe3aqFEjQz6+f2C96kWWL19usYh1ctMbW2tymnegz2Dr1q2GEGFJkiSRYcOGOXXvy4cXqaFDh/rSuAF7WP/aSVxzsZsT80iABEiABKJHgIrT6PGLiNZmJWORIkXEzZ2bub45HqodFLgqWrt2ra8I/UPxmSdPHl+e2w1cXpp/jG7evNmxiXmOWsUJEybI2LFjXden1eUnCZAACXghUKFCBXWgZNKkSZIlSxZLE7jsuXr1qiWfGSRAAiRAAiRAAiRAAuFNIGHChNKuXTvZt2+f4ICu3YHgPXv2hPciOXsSIAEDAShNzYpKQwWYqdy/L9jH0kumTJlk3LhxrvE0tfrY/4JCE/E39fLpp5/qk757uJE1C9z1ehGzm16EskKIGrPE9prM4+vTXp4B6pvjymbLlk1SpEih78rxHkYcMCzRBOvfv3+/lvR9hhIX36R4QwIkQAIkEG0CVJxGG2H4d2C2IDXHN9WvEJaj5hcPf4rTuXPnKmWlvp/Zs2dL8eLF9Vmu9/gBao4lgNgGTmJeE+q1b99e2rZt69SE+SRAAiQQZQLYNGvTpo3aNOvevbvgJCuFBEiABEiABEiABEggfhCABRoO6O7YsUNq1KgRPxbNVZJAPCTQpEkTeeqpp/yuHNaJ5kMT77//voqn6bfxfxVgedqsWTND9Z9//lmwL2eW6tWrWzzBYS/On8BL0vfff2+o1rJlS0FMZ7PE9prM42tpr88A9RGzVC/Hjh2T48eP67Mc72HwMWfOHJk2bZrvQjxUs4QKF/O8mCYBEiABEogeASpOo8cv7FsjAP2BAwcM63BTnMK1rtk1hVt9dPzuu+8a+q9SpYogEHugkjdvXkMTzUWHIfO/k31ma9R8+fLJRx99ZK7KNAmQAAnEKIF06dLJ6NGj1aZZrVq1YrRvdkYCJEACJEACJEACJBDaBAoXLixLly5Vigj8BqWQAAlEFoGBAwd6WtDChQsN9RIkSCCNGzc25HlJPPfcc4ZqMCCA+1mzwDKyVatWhuyjR48qd+KGTFNi8eLFcvnyZUOuk5ve2F6TYVK6hNdngCZ2IXVatGhhWbOue8MtlLQvvfSS70KoMrOEChfzvJgmARIgARKIHgEqTqPHL+xbIyaLWdwUoWZLTlhZ2b2IaH3ihJ1ZidmxY0etOKBPs8IWCgo72bt3r+UlCJZgSZMmtavOPBIgARKIcQKFChUS/Aj94YcfJH/+/DHePzskARIgARIgARIgARIIXQINGzaU3bt3q3j3qVKlCt2JcmYkQAKeCcAKs2jRop7qr1mzxlCvQIECglikgYqdpza4BreTqLjrNbvpLVasmJQpU8aue4mLNZknEsgzQNuqVauau5D169dL6dKlVYxaGJNEV0KBS3TXwPYkQAIkQAJWAomtWcyJTwTMsUBTpkxpCUCv52Gu7y8e6vTp0/XNJU2aNAKFwp9//mnI95I4c+aMoZpdLEFUMM8RJ++cTswZOmSCBEiABGKYQP369aVmzZry/9g7Dygpqq0LH3IGSRIkqQRFQIKAZBCUjAgIgiCSTCDPgPAAQfkFfCQBkSCKoCiCKCIgWXKUnHMOCpJz5l/7rlf1blVXdVf3TM9Mz+yzVtt1Y937VTN2165zztChQ9WNsytXrkTzGTgdCZAACZAACZAACZBAXCSAG/w9evRQnkpdu3aVSZMmxcVlck0kQAIeCRQqVMiS89JtGMLfImy3bvny5QvpPhjmwH06PeLa4cOH9anN4yJFiihBUPdIRbjeAQMGmH30A8w5c+ZMvcr13lls7cmyOCwDfXEAACAASURBVBHxeg2McQUKFBD8Jrfv8+DBgyqdFxw7kH4MAiui41WoUEEQoterxRUuXtfLfiRAAiRAAt4JUDj1zipe9rR7kOKpK+QTdTN7f3/5TZEg/YcffrBMdfnyZSlevLilLtQCnthzMvsacb6cOXM6dWUdCZAACYSdAG6a/fvf/xY8AdytWzefv4thXwBPQAIkQAIkQAIkQAIkEGsEENoRv4txg75z586OYTZjbXE8MQmQgGcCWbJk8dT37Nmzcu/ePUvfBQsWCBwPosPOnz/vOg1+c+rCKXJ8bt682fE+HMTEq1evmnPhXmDLli3Nsn4Qm3vS1+H1Guhjfv75Z0HI3VmzZunV6vju3bvKAxVeqP3791eR6ipWrCi1a9dWr0AexnGFi8/GWEECJEACJBBlAgzVG2WEkT2B3TvTX5heiJ52T1F//RGuAjkVwmVuAmwwewrX2jgvCZAACdgJ4AEOeOGvXLlS8ubNa29mmQRIgARIgARIgARIIB4TgCcTUuWMHTs2Hu+SWyMBEoCYFk67ceOG6/TNmzf3cYaAcOhkU6ZMsVTXqVNH3CK7xeaeLIsMoYC0XfC8ff/995X3rr8pbt26JYsWLZIPPvhAReODcDp+/HhBvZNFMhen/bCOBEiABEjgfwQonP6PRYI7QniPf/75x7Jvf0IocpXan5rz53Hq9uXMcsIoFJ588kmf0QiTsWXLFku9vz1ZOrJAAiRAAjFAoFy5coLcMTQSIAESIAESIAESIIGERSBx4sTSoUOHhLVp7pYEEhiBcKdnwX0vN4PwCW9J3ZzuzV26dElmz56td5M2bdpYynohNvekryPUY4ingwcPVs4dCF1cvnx5wd/jQIZc1W3btlUi6unTp326RzoXnw2xggRIgARIwCTgHpPV7MKD+ErA7pmJffoTQu39A+VDxRcM3dKnTy/jxo3Tq6J07CSIIgTJ7du3LfM69bN0YIEESIAESIAESIAESIAESIAESIAESIAESIAEokggQ4YMPjM0atRI4A0aHRYoehHC9eo5Pffs2SPbt29X4p9x/unTp4suwGbNmlXq1q1rNPu8x/aefBYUYgVC/SLnNF5wJEEI5cWLF8uSJUtk//79rrPu27dP8UG/NGnSmP3iCxdzQzwgARIgARIwCVA4NVEkvAN7LlB8UULCejez9y9VqpQkSZLErbvPlw48+dakSRPX/tHRYF9junTp5LHHHouOqTkHCZAACZAACZAACZAACZAACZAACZAACZAACbgSyJQpk09bqlSpwn4/zDhp/fr1JWPGjKLnQoXXaZEiRYwuMnnyZPMYBy+//LIkS5bMUqcXYntP+lqi6xj3QFu0aKFemPPYsWMyb948mTt3rvLGvX79uuVUiML34YcfytChQ836+MjF3BwPSIAESCCBEwgclyCBA4rP27d7kAbyzLT39+edev/+ffWlQ+f3wAMP6MWwHNvX+NRTT3kKvxGWxXBSEiABEiABEiABEiABEiABEiABEiABEiCBBEMgc+bMPiLk3r17Y2z/KVKkkGbNmlnOp4frRV7OhQsXWtr9helFx9jek2WxYSrkzp1b2rdvL2B14sQJee+993zOtHz5cktdQuBi2TALJEACJJCACNDjNAFdbH2rd+7ckY0bN+pV4k84xZcGvHTz1z9RokSCl2544i0UO3PmjCAfq2Hwci1RooRRtLzbhVN/a7QMZIEESIAESIAESIAESIAESIAESIAESIAESIAEokAAnpuI0LZmzRpzllCFU6Si2rJlizkPDhBVLW3atJY6ewHheseMGWNW79ixQ3bv3q3GTps2zZLiCvfXihUrZvZ1OogLe3Jal786CMSHDh0yu6RMmdLidWs2OBzg/uWQIUMEOUzHjh1r9ti2bZvcunVLkDMVFolczM3wgARIgARIwC8Bepz6xRN/G/Gl6dq1a5YN+vMgtQuSGOivP9qNLxLGSfQvLEZdoHeExqhevbqULl3afPXt29dxGMKQIO+AbhROdRo8JgESIAESIAESIAESIAESIAESIAESIAESCCeBSpUqWaa/ePGiCgFrqfRQGDVqlHkvDPfFMO+9e/cCjixXrpwUKFDA0s/wOrWH6Q3kbWpMEtt7Mtbh9R33PfV7ibiH6YWdPn/Tpk31ohJNjx49aqmLNC6WxbNAAiRAAiTgSoDCqSua+N1gzwWK3eILhZvZ+2fLlk3y5Mnj1l3VI8yFbki0Hqx42qlTJ9m6das5TdKkSaVXr15mWT9wEncpnOqEeEwCJEACJEACJEACJEAC8ZdA48aNBb8XjFf37t3j72a5MxIgARIggThLoEmTJj5r69evn0+dv4pLly6J3XEA+UvTp0/vb5jZBq9T3SCc/v3337JkyRKzGg4PyPPpxeLCnrys0+hTsmRJS+ouOI/s3LnTaPb0Du9S3VKnTi0PP/ywXuWYuzamr7VlQSyQAAmQAAlECwEKp9GCMfImsYuM+fPnF6ek5sbO1q1bZxyqd7dQuXqnypUr60V1PHPmTJ86t4pvvvlG8NKta9euUrx4cb3KPLbvKUeOHJIrVy6znQckQAIkQAIkQAIkQAIkQALxlwA8Se7evWu+gvUsib9kuDMSIAESIIGYJICH+O33xFasWCHz58/3vIz+/fsLUlfp5lXkxJiWLVtaUmgh5O+nn35q8bqEEIs8nV4sLuzJyzqNPghnjLDGun3xxRd6MeDx0qVLLX1wPxLpw3SLNC762nlMAiRAAiTgToDCqTubeN1iFxkDeWbaPUWRIzWQ1atXz6cLhM9A4inyELRr104lZdcnaNCggfTp00evshwHuyfLYBZIgARIgARIgARIgARIgARIgARIgARIgARIIBoIfPzxxxbhElM2bNhQvv/+e7+zwzPyzTfflAEDBlj6ValSRSB0erV8+fIJxug2YsQIvSivvvqqpRyoENt7CrQ+e3utWrUsVXDOGD58uNy/f99S71SYPXu26qu3lS9fXi+ax5HGxVw4D0iABEiABFwJUDh1RRN/G5Dc3B6eIlC+UoTz0G3hwoXy8ssvy6BBg1TCeT2crtHv+eefF7sge/PmTUEIrU8++USFB0GeBxjEUoQLGThwoHoiDF9m9C8yzz33nPz0008q7JYxv/2dwqmdCMskQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIxTaBatWrSs2dPy2mvX78urVq1kg4dOshvv/0mR44cUe24V7Zp0yYZP368lCpVSt1n0wdmzZpVJk2a5OPtqPdxOraH69Xvs2XPnl3swqLTHHpdXNiTvp5Axx999JHkzJnT7Hb79m155513pGbNmoLIerg/ardt27bJ22+/LS+88ILcuHHDbEa6Mvv1NBojjYuxbr6TAAmQAAm4E0jq3sSW+Epgw4YNKnyVvj+7wKm34ThjxoyCL3i64UsbXjA9R4LeB2EwqlatKnhizjB8Uendu7cqJkqUSM197tw5o9nnHU/I/frrr5IiRQqfNqPi8OHDcvr0aaOo3gPtydKZBRIgARIgARIgARIgARIgARIgARIgARIgARKIJgLwRNyzZ49MnTrVMuPXX38teMEyZMggV69eFbfIbokTJ5aJEydaBEDLZH4KyEvaqVMnyz05ozsEXOQED9Zie0/BrBf5YD///HOfPKQLFiwQvGAPPfSQIH3Z5cuX5ejRoz7hkdHHuAYPPPCA6+kjiYvrJthAAiRAAiRgEqDHqYki4RzYPTOTJUvmmjfUoPLMM88Yhz7v+AKBJ+KcrHTp0jJt2jRBwnknw9NubqJpypQpBQnV8WUGCdj9mX1PEGRxbhoJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJxDQB5MOcMmWKdOnSxfXUiMTmJprCWxIR3+AhGYqlS5dOeU46jQ02TK8xR2zvyViH13dEvevcubNr9xMnTghymW7cuNFRNE2TJo3AKcSes9Y+YaRxsa+fZRIgARIgASsBCqdWHgmiZBcZixUrJhAp/RnyINhzIxj9CxcuLEi67mb4grd+/XoJFA7YGI+5EAYY4TF69OghEHYDmX1PBQsWVE/tBRrHdhIgARIgARIgARIgARIgARIgARIgARIgARIIBwE82I80VxDn3JwO7OeF4Nm+fXvZsmWLIAxsVMwerhdzIUIb7uWFarG9p2DXjbymEyZMEITb9WrYY+vWrWXv3r0q56yXcZHGxcue2IcESIAEEiqB4GMyJFRS8Wjf9hAhXraGcBSLFi0ShMTdvXu3nDx5UvDUVe7cuQN6q2L+okWLyurVq2XFihXy888/qye5kDcV4X/xBB1CY+TKlUuJs3Xr1pVUqVJ5WZbZZ/DgwYIXjQRIgARIgARIgARIgARIgARIgARIgARIgASiQuDHH38UvKLL4LGIvJpr166VGTNmyJo1awT3xS5cuCDZsmVT98RwX6xChQrSqFGjgJHXvK7rueeeEz23qddxXvqFe0/ReQ0ggsJJY9asWbJs2TJ1HY4fPy7w+MW9SbB/9NFH5ZFHHlGvGjVqSMmSJb1g8OkTbi4+J2QFCZAACZBAtBOgcBrtSOPvhAjJa3yBCGWXePKqUqVK6hXKeI4hARIgARIgARIgARIgARIgARIgARIgARIggUgkgPtiTz/9tHpF4vqd1hxJe0JO14YNG6qX016isy6SuETnvjkXCZAACcQXAhRO48uV5D5IgARIgARIgARIgARIwCMBeDhcvnzZ7J0pUybJnDmzWb579656Ih/pFpA+AfnqEeIsUN55TLB161aZO3euHDlyRHlS3Lt3T4VGy5s3r8rR9cQTT5jn8XeACCdXr161dIFHRvr06S11XgqXLl2SU6dOWbpmzZpVEFXFi0XXnvRzheMa3Lx5U+bPny/79u2T/fv3q/dz586pa5slSxYVKQZpNJCqAzf0omrh4GJfE0LkwTMEnwdcQ3xOEfUGEW1w85lGAiRAAiRAAiRAAiRAAiRAAiRAAtFJgMKpiGzfvt3npkx0Qtbnwg0K3HCy3wTS+0TnMc6H3AU0KwFecysPlkiABEiABEiABBIWgXfffVcmT55sbrpnz57St29fVcb3pLZt26pwcmYHERk7dqyrcApxdPz48dK/f385ePCgPsxy/P7776v8Xq+//rq0a9dOENHEzcaNGye9e/e2NL/11lsycuRIS52XQq9eveTzzz+3dEWIvLJly1rq9EI49qTPH53XACH4cD179OihUmvo59GPEfKuW7duAvEaaS5q1aqlN3s6DobLa6+9JqNHj/Z7nd1O+tNPP8nAgQNlw4YNbl2UeAqObdq0ce3DBhIgARIgARIgARIgARIgARIgARIIhkCCF05xkwGx58+fPx8Mt5D7FixYUP75558YO1+hQoVUTtKQFxwPB/Kax8OLyi2RAAmQAAmQAAlEC4GZM2dKkyZN5NatW57nO3DggDRr1syvwKVPBiEMgho8WCGOPvbYY3qzedyyZUv56KOPLHm5pk2bJiNGjAhKiIPQBxFOt8cff9yvaBquPelrcDsO9hr89ddf0qBBA4F3sFfbsWOH1K5dWyBEf/HFF569T4PlArEdQry/62xfM7yDO3To4HPN7P1Qhjc0RH542X777bdOXVhHAiRAAiRAAiRAAiRAAiRAAiRAAkERcH/EO6hpIrczQj/FlGgKSoULF47R8zF8le9nk9fclwlrSIAESIAESIAESGDp0qXStGnToERTiGIVK1b0LJrqlFetWiUlS5YUvDvZww8/rB5w1NsQ3nblypV6VcBj7AvjdHv11Vf1ouU4nHuynMihEOw1QEjl5s2bu4qmKVOm9Csyjxo1St577z2HlfhWhcol0HXWzwTBHrnH7EK33sfpGN62uKYQyWkkQAIkQAIkQAIkQAIkQAIkQAIkEBUCCd7jFLmNlixZEhWGQY1FHqV33nknqDFR6ZwvX76oDI+XY3nN4+Vl5aZIgARIgARIgASiQABefhDgbty44TMLcopCoLPbiRMnpGrVqnL27FlLU8aMGaV169YqJG/x4sUFOTYRFhev5cuXy5kzZ8z+169fV96SEEMRKcVur7zyikBM1O3nn3+WSpUq6VV+j/WQxOiYJEkSadWqleOYmNiT44lFJJRrgBDEdj716tVTvzfAHvlAIUYePnxY4C36/fffy6RJkyxLGD58uIBziRIlLPV6IapcAl1n41zwgF28eLFRNN+RHxdRgpCCBBF8IOLi8wRP04sXL6p+CEOcLFkycwwPSIAESIAESIAESIAESIAESIAESCAUAgleOM2UKZNUqVIlFHYcE6EEeM0j9MJx2SRAAiRAAiRAAmEjgFCq165dM+dHqFR4/hUrVkxy5cpl1hsHSH0Asc0umkJI/e677yR37txGV/UO0Qt26tQpeeaZZ2Tnzp1mO+ZAvtMVK1aYdcYBwgZ36tRJILwZhnC9w4YN8xRe9s6dO/LLL78YQ9V7zZo1JUeOHJY6FGJqTz4n/m9FsNcAUXP+85//WKaD9+iQIUMsdcmTJ1diIwRHhOeFqN2iRQvz2mHfyI06Z84cyzijEBUuRYsWVWlKMJe/64x2fCa++eYb47TmO0LxwjM2RYoUZt1zzz2njg8dOiSNGjWSzZs3q/Lt27fNPjwgARIgARIgARIgARIgARIgARIggVAIJPhQvaFA4xgSIAESIAESIAESIAESiE8EDNEU0VEgTCI3ZZ06dRxFU+wbIt+iRYssCOCx+scff/iIpnqnbNmyqWgvRYoU0atV+N158+ZZ6lCAt+sLL7xgqT9+/LjyNrRUuhQWLlxoCoRGF7cwvTG1J2Md9vdgr8HGjRst+V/hbdm3b1/7tD5liI6ffPKJpR7sdXFab4wKl65du+pTuV5ndPr0008t+0Ed8q/i/Lpoqk+IcM4IBVy/fn29msckQAIkQAIkQAIkQAIkQAIkQAIkEDIBCqcho+NAEiABEiABEiABEiABEohfBCCa2oVK+w7hgWj3asySJYuMGDHCbz5NYx6kTYCgifybuo0ePVovmsfwbLUbwvV6MXuYXkQeadCggc/QmN6TzwK0Ci/XAN03bNigjRLlRZsqVSpLnVsBoYoRstgw7H/fvn1G0XyPKpc33njD03WG56j9WtWtW1c6duxorsXtAHuGwArPWhoJkAAJkAAJkAAJkAAJkAAJkAAJRJVAxIfqXbZsmacf1FEFxfEkQAIkQALhI4C/5TQSIAESIIHYJdC4cWOpVq1awEXAO3H37t2WfoMGDVL5NC2VfgrwPH3xxRdl4sSJZi/ktkRo3aRJrT9RatSoITlz5pSTJ0+afRF+1y7emo3/Pbh586ZMnz7dUo0QtU7eizG9J8uitILXa4AhyFmq29GjR+XYsWN+PX6N/mnTppUpU6bIlStXjCrH6xdVLjiPl+s8YMAAde3NxYhI79699aLf4zx58qhwz27iu9/BbCQBEiABEiABEiABEiABEiABEiABjYD1roTWECmH27dvF7xoJEACJEACJEACJEACJEACoRPwKlTNnj3bcpJEiRKpPJOWSg+FZs2aWYTTS5cuCcLPlilTxjIanpEvv/yyQJw17MiRI7Ju3TopXbq0UeXzPnfuXLl48aKl3i1Mb0zvybIoreD1GmDIU089pUIqa8PlpZdeEuwlQ4YMerXjMUTaQBYdXLxc50mTJlmWgjy09s+BpYNDoXv37kLh1AEMq0iABEiABEiABEiABEiABEiABIIiEHGhep2eEA9qx+xMAiRAAiQQ5wnwb32cv0RcIAmQQDwjgL+7TzzxhKddLV++3NIvf/78KheppdJDoVixYj699u7d61OHilDC9dpDvxYtWlRKlSrlOH9s7Mm+kGCuAcZWrVrVPoXK91myZEklqJ47d86nPdiK6OAS6DqfPn1aLl++bFnas88+ayl7KeTOnVsQMppGAiRAAiRAAiRAAiRAAiRAAiRAAlEhEHEep7Vr15ahQ4dGZc8cSwIkQAIkEMcJ4G89jQRIgARIIOYIFCpUyJLz0u3MCH+7detWS3O+fPlk165dljqvhdSpU8u1a9fM7ocPHzaP9YMiRYoIBEF4pBqGcL0I8epkmHPmzJmWJjdv09jak2VxIuL1GhjjChQoIPXr1/fZ58GDB+X111+Xt956S8qWLasE1sqVK0uFChUEoXO9WnRy8Xed7SGHsb68efN6XaalH0L2njlzxlLHAgmQAAmQAAmQAAmQAAmQAAmQAAkEQyDihFM8fTx//nyZM2eO4Mc8jQRIgARIIP4QgLcNRNNQPE3iDwXuhARIgARinoBXT72zZ8/KvXv3LAtcsGCBFC5c2FIXauH8+fOuQ+F1qgunENw2b94sxYsX9xkD0fTq1atmPfKmtmzZ0izrB7G5J30dXq+BPubnn38WhNydNWuWXq2O7969qzxQV61aJf3795fkyZNLxYoV1f9n8f/aQB7G4eSiX2cn4RQCaCiGcfpnJJQ5OIYESIAESIAESIAESIAESIAESCBhE4g44RSXCzfUeVM9YX9wuXsSIAESIAESIAESIIGYJwAxLZx248YN1+mbN28uXbp0kTt37ph9IBw6CadTpkwx++CgTp068uCDD1rqjEJs7slYQ6jvEEPhedujRw+V31P33rXPeevWLVm0aJF6ffDBB0rsBk/kj8U8dgsnF/06OwmnUfE4te+DZRIgARIgARIgARIgARIgARIgARIIhkDE5TgNZnPsSwIkQAIkQAIkQAIkQAIkEH0Erly5En2TOczkL6IMhE97KHcIp3a7dOmSzJ4921Ldpk0bS1kvxOae9HWEegzRc/DgwXLkyBEVurh8+fKSOHHgn3k7d+6Utm3bCsIgI8+o3cLJRb/Of/31l/3Uki5dOp86LxXwsqWRAAmQAAmQAAmQAAmQAAmQAAmQQFQIRKTHaVQ2zLEkQAIkQAIkQAIkQAIkQAKhEciQIYPPwEaNGgm8QaPDAnkaIlyvnrt0z549sn37diX+GeefPn26JaVH1qxZpW7dukazz3ts78lnQSFWINRv165d1euff/4RhFBevHixLFmyRPbv3+866759+xQf9EuTJo3ZL5xc9OucO3du85zGwfHjx6VgwYJG0fP7sWPHPPdlRxIgARIgARIgARIgARIgARIgARJwIkDh1IkK60iABEiABEiABEiABEiABHwIZMqUyacuVapU0qRJE5/6cFTUr19fMmbMKHqOTHidwmvSsMmTJxuH6h2haJMlS2ap0wuxvSd9LdF1DLG4RYsW6oU5ISjOmzdP5s6dq7xxr1+/bjnV+vXr5cMPP5ShQ4ea9THFpUCBAuY5jYOjR49SODVg8J0ESIAESIAESIAESIAESIAESCBGCQSO4RSjy+HJSIAESIAESIAESIAESIAE4iqBzJkz+4iQe/fujbHlpkiRQpo1a2Y5nx6uF3k5Fy5caGn3F6YXHWN7T5bFhqkAr8727dsLWJ04cULee+89nzMtX77cUhdTXNyEU8tiPBboceoRFLuRAAmQAAmQAAmQAAmQAAmQAAm4EqDHqSsaNpAACZAACZAACZAACZAACegE4LlZqlQpWbNmjVkdqnB6+/Zt2bJlizkPDh577DFJmzatpc5eQLjeMWPGmNU7duyQ3bt3q7HTpk0TzGtYiRIlpFixYkbR8T0u7MlxYX4qIRAfOnTI7JEyZUqL163Z4HAAj90hQ4YIcpiOHTvW7LFt2za5deuWIGcqLLq4BLrOTsLpsmXLVP5Vc3EeDg4fPixnzpzx0JNdSIAESIAESIAESIAESIAESIAESMCdAD1O3dmwhQRIgARIgARIgARIgARIwEagUqVKlpqLFy+qELCWSg+FUaNGSenSpc0X5r13717AkeXKlRO72GZ4ndrD9AbyNjVOFtt7Mtbh9R1isc6ubNmyntjp8zdt2lQvKtEUIXJ1iw4uga4zhPIcOXLop5UpU6YIxOFg7LPPPgumO/uSAAmQAAmQAAmQAAmQAAmQAAmQgCMBCqeOWFhJAiRAAiRAAiRAAiRAAiTgRMApn2m/fv2currWXbp0Sfr27WtpR/7S9OnTW+rcCvA61Q3C6d9//y1Lliwxq+E5iTyfXiwu7MnLOo0+JUuWlMSJ//dT7tq1a7Jz506j2dM7vEt1S506tTz88MN6lWPu2mCu9c2bNz1d51atWlnOe+PGDfn6668tdf4KEFnHjRvnrwvbSIAESIAESIAESIAESIAESIAESMATgf/92vbUnZ1IgARIgARIgARIgARIgAQSMoEyZcpI5cqVLQhWrFgh8+fPt9T5K/Tv398nrKpXkRPztmzZUhIlSmSeAiF/P/30U4vXJYRY5On0YnFhT17WafSBlybCGuv2xRdf6MWAx0uXLrX0KV68uCRJksRSF1UuyJtqD5/rdJ179OghWbJksZx76NChsmvXLkudW6FXr14C8ZhGAiRAAiRAAiRAAjFNoHHjxpI0aVLz1b1795heAs9HAiRAAiQQzQQonEYzUE5HAiRAAiRAAiRAAiRAAvGdwMcff2wRLrHfhg0byvfff+936xC33nzzTRkwYIClX5UqVQRCp1fLly+fYIxuI0aM0Ivy6quvWsqBCrG9p0Drs7fXqlXLUvXNN9/I8OHD5f79+5Z6p8Ls2bNVX72tfPnyetE8jgoX+41Dt+ucIUMG6dOnj3lOHJw6dUoQKnjt2rWWer2A/KmtW7eW0aNH69U8JgESIAESIAESIIEYI4BUE3fv3jVfXlJPxNjieCISIAESIIGQCFA4DQkbB5EACZAACZAACZAACZBAwiVQrVo16dmzpwXA9evXBSFXO3ToIL/99pscOXJEtSNc66ZNm2T8+PFSqlQpGTNmjGVc1qxZZdKkST7ejpZODgV7uF5dMMyePbvYhUWHKSxVcWFPlgUFKHz00UeSM2dOsxdExHfeeUdq1qwp69atkytXrphtxsG2bdvk7bfflhdeeEEQDtewPHny+FxPoy0qXNavX29MI4GudjqkkwAAIABJREFU82uvvebjRYsQvNWrV5du3brJtGnT5NixY2q+/fv3y4QJE6RGjRry3XffmecoXLiwecwDEiABEiABEiABXwJjx44V5AU3XsuWLfPtxBoSIAESIAESSOAEkibw/XP7JEACJEACJEACJEACJEACIRCAJ+KePXtk6tSpltHITWnkp4Qn4dWrV+XOnTuWPkYBeTonTpxoEQCNtkDvyEvaqVMnxxCtEHARMi1Yi+09BbNe5IP9/PPPffKQLliwQPCCPfTQQ5I/f365fPmyHD161CdsLvoY1+CBBx5wPX1UuRjn0IVe+8lwvSCuQ/hFDlzD8PkZOHCgURTkYnUKy9u0aVMVQhqfCRoJkAAJkAAJkIAzAUR4OHnypNnYpUsXnxQMZiMPSIAESIAESCCBEqDHaQK98Nw2CZAACZAACZAACZAACUSFAPJhTpkyRXDDzc0uXrzoKppCRFu4cKESytzG+6tPly6d8px06hNsmF5jjtjek7EOr+/IqdW5c2fX7idOnBDkMt24caOjaJomTRpBblR7zlr7hFHhEsx1fvrpp9Vnwl9uWifR9LnnnlOiq33dLJMACZAACZAACZAACZAACZAACZBAsAQonAZLjP1JgARIgARIgARIgARIgAQUgUSJEsmgQYOUOIcwvF4Mgmf79u1ly5YtgjCwUTF7uF7MVaZMGYlKyNbY3lOwPJDXFGFrEW7Xq2GPyA26d+9elXPWy7hQuEAIDfY6ly5dWnbs2CHNmjULuCx4nw4ePFjmzp2rPFEDDmAHEiABEiABEiABEiABEiABEiABEghAIPj4VQEmZDMJkAAJkAAJkAAJkAAJkEDcJvDjjz8KXtFl8FhEXs21a9fKjBkzZM2aNfL333/LhQsXJFu2bJIrVy71qlChgjRq1CjaRC54Guq5TaNrP5gn3HuKzmsAEfTll1+WWbNmCXKV4TocP35c4PGL3LPg/+ijj8ojjzyiXsgNWrJkyZBwBcMF4meyZMmCPg8+M5MnT5bevXurz+mKFSvUfpCXFeGHIRLXq1dPeRxDiDesY8eOgheNBEiABEiABEiABEiABEiABEiABEIlQOE0VHIcRwIkQAIkQAIkQAIkQAIkYBKARyI8DPGKLxZJe0KO0IYNG6pXuPnHFBd4Dn/yySfh3g7nJwESIAESIAESIAESIAESIAESIAGTAEP1mih4QAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkFAJ0OM0oV557psESIAESIAESIAESMBCAOFNY8rSpEkjV69ejanTCc5XpEiRGDsfT0QCcZEA/43HxavCNZEACZBA5BHYunWryq995MgRlZrg3r17Kox83rx5pWbNmvLEE0942hRC0B87dsynb5YsWSRjxow+9W4VZ8+elStXrqjw+MmTJxe8UqRIoV5Im/DPP/+YQ+/cuWMe4+D8+fOyb98+sw75wxEW34tFFwf9XOABLoY9+OCDkiFDBqOo3g8ePCjTp0+XAwcOyMmTJwWRMHLkyCH58uVTkTcKFChg6R9KAXngkf4A8586dUoyZ84suXPnlqJFi8ar6CqhsOEYEiABEkgIBCicJoSrzD2SAAmQAAmQAAmQAAkEJBCTIWZLlCghmzZtCrim6OqA823cuDG6puM8JBCRBPhvPCIvGxdNAiRAAnGCAMTR8ePHS//+/QXCnZu9//77UqpUKXn99delXbt2kjixe7C/JEmSyEsvveTzHQ3i3IYNGzzlCYdg+tRTT8nhw4ctS0Ko+w8//FAmTJgg7777rqVNL4wbN07wMgzC79y5c42iz3s4OOgnQX701atXm1Vjx46VDh06qPL69eulc+fOlnaz438PunbtKhUqVFB7KlSokL05YPmnn36SgQMHKv5unXF9wLRNmzZuXVhPAiRAAiQQ4QTc/+8d4Rvj8kmABEiABEiABEiABEiABEiABEiABEiABEiABEggKgTg2VimTBlp3769X9HUOAdEz9dee00qVaoku3fvNqp93pMlSybfffed8gzVG7dt26YEWr3O7bhbt24+omm5cuWke/fubkNCrg8Xh0ALun//vhKB8QCULqq6jVu5cqUSr+fPn+/Wxaf+0qVLAtEWL1w/f4br07ZtW2nevLncunXLX1e2kQAJkAAJRCgBepxG6IXjskmABEiABEiABEiABKKXwJIlS6J3Qj+zpU2bVoVU89MlWptwPhoJJHQC/Dee0D8B3D8JkAAJBE9g+/bt8uyzz6qQvMGOXrVqlZQsWVIWLlwo5cuXdxyOsL79+vWTLl26WNrh2dq4cWO/qRaWLl0qo0ePtozDd76JEycKvFmj08LNwW2tEE3/9a9/yYgRI9y6ONYjJUarVq0E4YSzZcvm2MeohPjZsGFDWbx4sVHl6X3y5MkqTDC8cGkkQAIkQALxiwCF0/h1PbkbEiABEiABEiABEiCBEAlUqVIlxJEcRgIkEAkE+G88Eq4S10gCJEACcYfAiRMnpGrVqoIcoroh/2jr1q2VV2Px4sXl3LlzsmbNGvVavny5nDlzxux+/fp1adCggcAL0i10LMK+zpgxQ+XUNAZCzEOoX4ivTiLotWvXVDuERd2GDh0qjz76qFmF//ehzrCPPvpI4F1pWLVq1dT6jDLytNotpjjYz4vyZ599Jnv27DGbkIO1ZcuWUqxYMcFakbN1xYoVsmjRIp80GKdPn1aeu8OHDzfHOx289dZbjqIp5q9cubLyNi5YsKBAPMZ1hifrxYsX1VQ//vijp5DKTudlHQmQAAmQQNwlQOE07l4browESIAESIAESIAESIAESIAESIAESIAESIAESCCGCSBc64svvmgRTYsUKSLvvfeeNGrUSDJkyGBZEQQ2GHKOwgt08ODBAuEOBuEVXqsQ9/Lnz6/q9P8gD+r06dNVLs9ffvnFbPrzzz+VqAkP0kyZMpn1R48eVSFlETrXsDRp0ggEQoituiHPPV6GDRo0yCKcIh/rO++8YzT7vMckB5+Ti5iiKdaJ/KX16tUTiKe6wVsUAjJETPSB0GvYt99+q8IWZ8+e3aiyvH/55ZeWHK9orFOnjgq1XLduXUma9H+3zp977jk19vz58/LDDz8oL9i9e/fK7du3LXOyQAIkQAIkEPkEmOM08q8hd0ACJEACJEACJEACJEACJEACJEACJEACJEACJBANBCDCwavx0KFD5mxZsmQRhHxv06aNj2hqdhIRhMr94IMPVIjYlClTmk3Hjh3zCcdrNooIvFh//vlngVin2+zZs5VQa3iW3rhxQ4mw8HzUDWFj7aKp3h7KcWxwcFpnjhw5lDdu06ZNfURTo3+iRImkRYsW0rdvX6NKvcMzFN6+Tobr26lTJ0sT+P/+++/y/PPPW0RTvROuFcYtWLBAkidPrjfxmARIgARIIJ4QoHAaTy4kt0ECJEACJEACJEACJEACJEACJEACJEACJEACJBA1AvPmzZPdu3dbJoGnZubMmS11/grIqwmPVd2QQxOhZf3Z119/7XMe5DIdM2aMGtarVy+Bl6Nur732mvLE1Oui4zg2Oejr7927t6tgqvfDcY0aNexVltDJeuOAAQN8rgfO5dXy5MkT7WK113OzHwmQAAmQQHgJUDgNL1/OTgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkECEE4OWpG7wZEZ43WGvWrJllCHKLbty40VJnLyCk7KhRo+zV0q1bN5k6darK+ak3FihQwKdOb4/KcWxyMNYN78727dsbxYDv4IfrpZs9R63RNmnSJONQvdesWVPlM7VUBih07949QA82kwAJkAAJRCIBCqeReNW4ZhIgARIgARIgARIgARIgARIgARIgARIgARIggWgnsHz5csucyEuaPn16S52XQrFixXy62b1FfTqICELSNm/e3NJ0+fJlVX/v3j2zHvk3kf8U+U3DYbHNAXsqVKiQa8hcpz2DSbp06SxNEKzthvyzYKob8tAGa7lz5xaEcaaRAAmQAAnELwL/y3Adv/bF3ZAACZAACZAACZAACZAACZAACZAACZAACZAACZCAZwI3b95U+Un1Afny5ZNdu3bpVZ6PU6dOLdeuXTP7Hz582Dz2dzBy5EhBiN6TJ0+6duvZs6eULVvWtT0qDXGFA/KbBmtJkiSxDDHyw+qVBw4c0IvqOG/evD51XioQsvfMmTNeurIPCZAACZBAhBCgcBohF4rLJAESIAESIAESIAESIAESIAESIAESIAESIAESCB8BhHXVvTpxpgULFkjhwoWj5aTnz5/3NA9C1I4bN05q167t2L9MmTLy4YcfOrZFR2Vc4ZAqVaro2I7PHE7CKQTQUAzjAoVgDmVejiEBEiABEog9AgzVG3vseWYSIAESIAESIAESIAESIAESIAESIAESIAESIIE4QsAtH2Z0Le/GjRuep6pVq5bUrVvXsf/w4cODCmHrOImfyrjCIUWKFH5WGXqTk3AaFY/T0FfCkSRAAiRAAnGRAIXTuHhVuCYSIAESIAESIAESIAESIAESIAESIAESIAESIIEYJXDlypWwng8hcL3atm3blLerU/9hw4Y5VUdbXVzhkChRomjbkz7RX3/9pRfVsT03qk8Hl4q7d++6tLCaBEiABEggUgkwVG+kXjmumwRIgARIgARIgARIgARIgARIgARIgARIgARIINoIZMiQwWeuRo0aSfPmzX3qQ6nw6tV469YteeWVVwTvTjZlyhRp2LChvPTSS07NUa6LKxyivBGXCXLnzu3Tcvz4cSlYsKBPfaCKY8eOBerCdhIgARIggQgjQOE0wi4Yl0sCJEACJEACJEACJEACJEACJEACJEACJEACJBD9BDJlyuQzKfJsNmnSxKc+nBV9+vSRzZs3+z1Fx44dpXLlypIzZ06//UJpjCscQlm7lzEFChTw6Xb06FEKpz5UWEECJEACCZMAQ/UmzOvOXZMACZAACZAACZAACZAACZAACZAACZAACZAACWgEMmfOLMmSJdNqRPbu3Wsph7uwZs0aGTBggOU0efLkUR6meuW5c+ekXbt2elW0HccFDtG2GYeJ3IRTh64Bq+hxGhARO5AACZBAxBGgcBpxl4wLJgESIAESIAESIAESIAESIAESIAESIAESIAESiG4CEE1LlSplmTZU4fT27duyfv16yytQ7tBr166pEL32vJmjR4+Wr776SrJmzWpZ29y5c2XMmDGWuugoxDaH6NiDvzmchNNly5b5G+LYdvjwYTlz5oxjGytJgARIgAQilwCF08i9dlw5CZAACZAACZAACZAACZAACZAACZAACZAACZBANBKoVKmSZbaLFy8KBMpgbdSoUVK6dGnzhXnv3bvnd5quXbvKvn37LH1atGghderUkSxZssjIkSMtbSh06dJFDhw44FMf1YrY5BDVtQcanzZtWsmRI4elG/LGnj171lIXqPDZZ58F6sJ2EiABEiCBCCRA4TQCLxqXTAIkQAIkQAIkQAIkEF4CL774ojz66KPm64033gjvCWN49saNG0vSpEnNV/fu3WN4BTwdCZAACZAACZAACcRNAk75TPv16xfUYi9duiR9+/a1jKlfv76kT5/eUqcXFixYIBBbdUPI3GHDhplV+I6K73G6Xb16VVq3bh1QlNXHeDmOLQ5e1hYdfVq1amWZ5saNG/L1119b6vwVILKOGzfOXxe2kQAJkAAJRCgBCqcReuG4bBIgARIgARIgARIggfAROHHihBw8eNB8/f333+E7WSzMDG8HhIAzXoG8H2JhiTwlCZAACZAACZAACcQKgTJlykjlypUt516xYoXMnz/fUuev0L9/f58QrvAcdbMLFy5I27Zt5f79+5YuQ4cO9QnPC3EVgqpuK1eulEGDBulVjseJEiWy1N+6dctS1guxwUE/f7iPe/Toobx49fOA965du/Qq1+NevXoJQivTSIAESIAE4h8BCqfx75pyRyRAAiRAAiRAAiQQ7wggH9TMmTMFN610O3/+vF7kMQmQAAmQAAmQAAmQAAlEmcDHH38sdpGxYcOG8v333/udG0Lam2++KQMGDLD0q1KlisDj1M06deokx48ftzTXrFlT7F6R6PDggw/KiBEjLH1R6N27t2zbts2nXq/AWN3WrFnj11M1pjnoawv3cYYMGaRPnz6W05w6dUoQonjt2rWWer2A3LXw8EXeWRoJkAAJkED8JEDhNH5eV+6KBEiABEiABEiABCKaADwgN23aJAiLVr16dcmUKZM0aNBAtm7datnXhg0bLGUWSIAESIAESIAESIAESCCqBKpVqyY9e/a0THP9+nUlZHbo0EF+++03OXLkiGq/efOm+t46fvx4KVWqlIwZM8YyLmvWrDJp0iRJkiSJpd4o/Pzzz/LDDz8YRfWeJk0an3n0Ds2bN5fnn39erxJ4j0Jo9edFmidPHsuYP//8U2rUqCEQSL/88kv59ddfLe0xycFy4hgqvPbaa/LYY49ZzoYQvPj90a1bN5k2bZocO3ZMte/fv18mTJigeH333XfmmMKFC5vHPCABEiABEogfBJLGj21wFyRAAiRAAiRAAiRAApFOAE/Z//HHHzJnzhxZuHCh4KYFjQRIgARIgARIgARIgARigwDExD179sjUqVMtp0ceTCMXJrwWkWP0zp07lj5GIXHixDJx4kTJmTOnUWV5h4cjPFTthvyo+fLls1dbyhBoly1bJnoEli1btigRFKGCnaxWrVpK9NXbFi9eLHjB4OX6wgsv6M1qvnBzsJwwBgtJkyYVCN7YN/LSGoZrOnDgQKMoqVOndgzL27RpUxXWGR7DNBIgARIggfhDgB6n8edacickQAIkQAIkQAIkEFEEEMps9uzZ0qVLFylatKjkzp1bXn31VZkyZYpn0bR06dJh2TOetj969Kj5Mm6OheVknJQESIAESIAESIAESCDOEYCHKL6X4ruqm128eNFVNIVYiocBIcq5Wfv27X1yoSK3aOfOnd2GmPXZs2eX4cOHm2XjAILf6tWrjaLlHSFms2XLZqkLVIgJDoHWEM72p59+Wl0ne95Y/ZxOuUyfe+45Jbrq/XhMAiRAAiQQPwhQOI0f15G7IAESIAESIAESIIE4T+D+/fsq1O7gwYMFNxoyZswodevWlSFDhsj27dsDrj958uSSI0cOSz885R8Oww0lCLnGK0uWLOE4DeckARIgARIgARIgARKIwwSQ53TQoEGydOlSFYbXy1LTpUsnEETh/YlQt242btw4mTVrlqU5WbJkypsVnqpeDKF569WrZ+l69+5dlYPTSexLlSqVIK9p2bJlLWMCFcLJIdC5Y6IdD2Pu2LFDmjVrFvB08D7F75m5c+cqT9SAA9iBBEiABEgg4ggwVG/EXTIumARIgARIgARIgAQih8Bff/0lixYtknnz5qnX6dOng1p8gQIFlMiKJ/WRa+iDDz6QUaNGBTUHO5MACZAACZAACZAACZBAVAhUrlxZ1q1bJ2vXrpUZM2Yo8fHvv/+WCxcuKA/OXLlyCV4VKlSQRo0aeRLU2rVrJ3hF1WbOnBnUFAgBvGrVKhVZBSF4kcMTYiDWX6JECb9zhYODfkKsK6p27ty5kKbAg5OTJ0+W3r17y48//igrVqwQpBK5ceOGPPTQQ4L8sBCpEcoY4rhhHTt2FLxoJEACJEAC8YcAhdP4cy25ExIgARIgARIgARKIdQK4sbBy5UqZP3++em3evDmoNWXKlEk9mQ+P1Nq1ayuPz6AmYGcSIAESIAESIAESIAESCAMBeF0irCtekW7waIWAGiiPqtM+4xMHp/0VLlxYPvnkE6cm1pEACZAACSQQAhROE8iF5jZJgARIgARIgARIIFwEdu7cqbxJIZYuWbJEPZXt9VwIR4bQWPAohViKnE5eQ5N5PUco/W7fvi0ILWwY1pQ0qfNXZzylD8HYsAcffFDsIYQPHjwo06dPlwMHDsjJkycFN5wQdhg3qxo2bCjwrI2q7d27V5YtW6bmP3XqlCBPE0INI39suG7wbd26VYUpO3LkiMDr4t69e+pp/Lx586pr+sQTT3jaFviBo90QIhkhnb3a2bNn5cqVK4LPFUI745UiRQr18joH+5EACZAACZAACZAACZAACZAACZAACSRcAs53fxIuD+6cBEiABEiABEiABEggAAGE20X4XQilc+bMUYJZgCGW5kceeUSJpBBKa9SoYQl1ZekYi4UqVarI6tWrzRU8//zzSvg0K7QD5ELS+44dO1Y6dOigeqxfv146d+5sadeGqsOuXbuqsG7Ic1WoUCF7c8DyTz/9JAMHDpQNGza49oV4+u6770qbNm1c+3htgDg6fvx46d+/v0AQdrP3339f5QJ7/fXXVRg6f4J4kiRJ5KWXXpKNGzdapsO6sS8IoYEMgulTTz0lhw8ftnSFx8CHH35oqWOBBEiABEiABEiABEiABEiABEiABEiABJwIUDh1osI6EiABEiABEiABEiABk8DNmzeV8GeE34W4pXtjmh1dDh544AGpWrWqEksRfjeUkGAuU8fZavDp1auX/Oc//5G7d+8GXCfCG5cqVUqmTZumOAUcICKXLl1SAi2E00C2bds2adu2rRK7v/3220DdXdvhMQuh2J9Iqw9Gv9dee00mTJggEIYfe+wxvdk8hjD63XffKQb4vBmGdUOg/eijj4wq1/du3br5iKblypWT7t27u45hAwmQAAmQAAmQAAmQAAmQAAmQAAmQAAnoBCic6jR4TAIkQAIkQAIkQAIkoAjs2bPHzFMK79Jr1655JgPvQXj+waMUL4hXqEsoBtH0X//6l4wYMSKoLV+9elVatWolCH+bLVs2v2Nv3bqlQvwuXrzYbz974+TJk1WYYHiNBmvbt2+XZ599NmgPY5xn1apVUrJkSVm4cKGUL1/e8dQI69uvXz/p0qWLpR3CaePGjaVIkSKWer2wdOlSGT16tF4ladOmlYkTJyaoz54FAAskQAIkQAIkQAIkQAIkQAIkQAIkQAJBE6BwGjQyDiABEiABEiABEiCB+EcAuSGN8Ltz586V48ePB7VJ5LQ0hFKIa/Ycn0FNFuGdP/vsM4HwbFjq1KmlZcuWUqxYMQGnO3fuyIoVKxTvTZs2Gd3UO8IgQygcPny4pd5eeOutt8RJNMX8lStXVrliCxYsKBA716xZo0Twixcvqml+/PFHT6Fv9XOeOHFCeQ3jc6Ib8o+2bt1aeYoWL15czp07p86Hcy5fvlzOnDljdr9+/bo0aNBA4F3rFpIY4YRnzJihcrUaAyESt2vXTomvTgI8RH20272ghw4dKo8++qgxDd9JgARIgARIgARIgARIgARIgARIgARIICABCqcBEbEDCZAACZAACZAACcQ/Ardv35a1a9eaXqXr1q2TYLwQ06dPrwQ6iKV16tShQKV9RHTRtGnTpjJy5EjJkiWL1kOUtyh4v/jiiyo8r944ffp0v8Lpzp075ZtvvtGHqGOE4h01apSkSJHCbMP1gR06dEgaNWokmzdvVmVcf68GQfKVV14Ru2iK8MsIr5s7d27LVBBuYadOnZJnnnlGsF7DMAdETgjHToY8qAjrC5EZOUsN+/PPPwVCqN0bFe09evQQhBDWDTlp27dvr1fxmARIgARIgARIgARIgARIgARIgARIgAQCEkgcsAc7kAAJkAAJkAAJkAAJxAsC+/fvV8Jaw4YNJVOmTFKpUiX55JNPlIAaSDSFoIXwuxCplixZokS0mTNnyttvv03R1OXTgdye8O60i6ZGdzCdNGmS8g416vB+9OhR2bJli15lOf700099vCu/+OILlUNUF031QQ8//LDy2Kxfv75e7ekYuUnhjaxb8+bN5Y8//vARTfU+CDeMz4o9xC48TufNm6d3tRxjrfDatVvv3r0Fn2HdMJc9JDLO+9VXX+ndeEwCJEACJEACJEACJEACJEACJEACJEACngjQ49QTJnYiARIgARIgARIggcgjcP78eSVczZ8/X+bMmSNHjhwJahN58uSR6tWrqxC8NWvWFIRlpXkjkCNHDuUhCXHUn0HofPPNNwUelbpBIHzyySf1KnUMz1HkKdWtbt260rFjR73K8ThVqlQCgRWiJcLfejF4mw4ZMsTSFUIwxMpAe8OgrFmzqrym+fLlkxs3bpjzIB8pPlNu1qFDB/ntt9/k999/N7sg1C+8SBGiOFGiRGo+eNnaRf+vv/5andccyAMSIAESIAESIAESIAESIAESIAESIAES8EggIoXTBQsWqJt/N2/e9LhNdiMBEiABEogEAhAQateuLciPSCMBEgieAHJnIuQuhFK8EIr37t27nidKkyaNGX4XIV4LFy7seSw7WgnAOxK5Tb1YjRo1fLrpuUH1xgEDBqgcqXodzuXVIIYjVC6ESy8GkXX37t2WroMGDZLMmTNb6vwV4AGKkMQTJ040u0H8xOc1aVL3nyMQQOGtqocIXrp0qYwZM0aJzb169ZK9e/eac+IAXr716tWz1LFAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAl4JuN+p8DpDDPeDaGrkaorhU/N0JEACJEACMUAAOewg+FA8jQHYPEW8IHD48GH1bwYC18KFC+XSpUue9wWvPXg14rsVvP8QujdZsmSex7OjMwF45gaTXzN79uzKgxLenYbpYqFRh3eE9tUN161MmTJ6VcDj7t27exZOZ8+ebZkPnxnkSg3WmjVrZhFO8TnduHGj37WDC3K2Yqxu3bp1U+GP7eF8CxQo4BjiVx/LYxIgARIgARIgARIgARIgARIgARIgARLwRyDihFOEmaORAAmQAAnEbwL4W0/hNH5fY+4udAIQnJA30gi/e/DgwaAme+ihhyzhd93ybwY1KTtbCBQqVMivJ6Wls4jqmy5dOovo7SSAnz59Wi5fvmwZHsrfyty5cyvh0c2rVT/B8uXL9aLkz59f0qdPb6nzUihWrJhPN3iLBhJ9mzZtKtOnT1csk4KkAAAgAElEQVS5Yo0JwAD1usFzFR6t8JqmkQAJkAAJkAAJkAAJkAAJkAAJkAAJkECoBCJOOGV43lAvNceRAAmQQOQQ4N/6yLlWXGn4CSDULjzz4FEKsXT16tU+oVr9rQJ5LStWrKg8SuFZWrRoUX/d2RYNBJDfNFhLkiSJZYjufWo0HDhwwDg03/PmzWseB3OAkL2BhFP8Ld66datlWuQq3bVrl6XOawGhi69du2Z2h7e0Fxs5cqQgRO/Jkyddu/fs2VPKli3r2s4GEiABEiABEiABEiABEiABEiABEiABEvBCIOKEU/umkPeocuXK9mqWSYAESIAEIojAsmXLZPv27RG0Yi6VBMJL4MiRIyrsLoRSvC5cuOD5hAiliu9HEEnxqlKliiB/MC3mCECsDoc5CacQQEMxjIMg788QLvjevXuWLkibEV25b8+fP2+Z262A0Mfjxo1TObCd+sBr9cMPP3RqYh0JkAAJkAAJkAAJkAAJkAAJkAAJkAAJBEUg4oVTiKZ4Cp1GAiRAAiQQuQQ6duxI4TRyLx9XHg0Erly5ojzqDKF09+7dQc2KXJDVq1c3c5Vmy5YtqPHsHL0EwiVUOwmnUfE4DbRrtzyrgcZ5bb9x44bXrlKrVi2pW7eu/P777z5jhg8fHlRoZJ8JWEECJEACJEACJEACJEACJEACJEACJEAC/yUQ8cIpryQJkAAJkAAJkAAJRBoBePFt3rxZeZNCLF2xYoXcvn3b8zZSpkwp5cuXN71KS5Qo4XksO4afALx+w2F//fWXz7TIjRqKIQR0IIOgH04LJiz7tm3bBN6uTjZs2DCZPHmyUxPrSIAESIAESIAESIAESIAESIAESIAESCAoAhROg8LFziRAAiRAAiRAAiQQGoHjx49bwu8G682H8KhG+N2qVatKuMLBhrY7jooJArlz5/Y5DT5XBQsW9KkPVHHs2LFAXSRDhgw+fRo1aiTNmzf3qQ+lwqu37K1bt+SVV14RvDvZlClTpGHDhvLSSy85NbOOBEiABEiABEiABEiABEiABEiABEiABDwToHDqGRU7kgAJkAAJkAAJkIB3AteuXZPly5ebXqXB5vHNmjWrPPPMM0osRZjSnDlzej85e8ZLAgUKFPDZ19GjR8MmnGbKlMnnfBDsmzRp4lMfzoo+ffooD21/50DId6Tw4L8Tf5TYRgIkQAIkQAIkQAIkQAIkQAIkQAIkEIgAhdNAhNhOAiRAAiRAAiRAAh4I3L9/X7Zu3WoKpRBNgwlFmjx5cilXrpzpVVqqVCkJV8hXD9thlzhIwE04DWWpXjxOM2fOLMmSJbOEkd67d28opwt5zJo1a2TAgAGW8Xny5JGSJUvK9OnTzfpz585Ju3btZM6cOWYdD0iABEiABEiABEiABEiABEiABEiABEggWAIUToMlxv4kQAIkQAIkQAIk8F8CyDn5xx9/KLF03rx5cvr06aDYIMSqEX4X3qVp0qQJajw7JywCTsLpsmXLpG3btkGBOHz4sJw5cybgGIimEPAhXhoWqnCKHL5btmwxplHvjz32mKRNm9ZSpxfgtY0QvfZ8rKNHj5YyZcrIypUr5Z9//jGHzJ07V8aMGSNvvPGGWccDEiABEiABEiABEiABEiABEiABEiABEgiGAIXTYGixb7wjcO/ePUmcOHG82xc3RAIkQAIkEB4CN27ckBUrVphepXYhKNBZEfpUD7/rlLMy0BxsT7gEIDLmyJFDINgbhvyeQ4YMEXiHerXPPvvMa1epVKmSRTi9ePGiQKBE+OhgbNSoUfLOO++YQ1KmTCmnTp0yy04HXbt2lX379lmaWrRoIXXq1FF1I0eOlKZNm1rau3TpIs8++6w8+uijlnoWSIAESIAESIAESIAESIAESIAESIAESMALAQqnXiixT0QTuHPnjixZskQOHDgghw4dUq+DBw+q9wsXLgjCvcHjBy94cpQuXVqefvrpkPa8fv1613ElSpSQJEmSuLazgQRIgARIIG4S2LFjh+lRunTpUoF46tXgsQfPOHiV1qxZU/0/hg/seKXHfk4EWrVqJQMHDjSb8Hn8+uuvpVu3bmadv4OzZ8/KuHHj/HWxtCGf6aBBgyx1/fr1C0o4vXTpkvTt29cyR/369SV9+vSWOr2wYMECgdiqG8ThYcOGmVUvvviiNG7cWH755Rez7urVq9K6dWuBJy7/rZlYeEACJEACJEACJEACJEACJEACJEACJOCRAIVTj6DYLfIIICTct99+K/3791ciqdsODDEVIRYNa9CggQwePFgJqUZdoHfkCoPo6mQPPPCAIPcWzTuB8+fPyyOPPCLIGWi3VKlSCXgnTco/YXY2LMceAXwm6T0Ye/yj88wIt6uH39W9+7ycB55uRvjd6tWrS7p06bwMYx8S8ESgR48e8s0331hC7Q4dOlTw3eXxxx8POEevXr0EIXC9GoT/ypUrKyHSGGN4XeNz7sXwXcweGhieo26GB9sQftj+HQD7zJo1q2UYxFU8IAdB2DCE8IXY61VMNsbxnQRIgARIgARIgARIgARIgARIgARIgAQYo5SfgXhH4NatW/Lll18q0bNDhw5+RVO3zc+YMUOKFCkiCPeGkHRebO3ata7dIKgmSpTItZ0NvgT+/PNPwY1T8Le/8uXLR9HUFxlrYpHAxIkTpWHDhrG4Ap46KgRu3ryphJfu3burfI7Zs2cXiDoTJkywhER1OwcejsH1R95FPIyzf/9+5SmHOoqmbtRYHyqBDBkySJ8+fSzDEfIWIXX9fRfBA2XwxMTnNFj7+OOPfb7H4PP9/fff+50KAu2bb74pAwYMsPSrUqWKwOPUzTp16iTHjx+3NMNjG962dnvwwQdlxIgR9mrp3bu3bNu2zaeeFSRAAiRAAiRAAiRAAiRAAiRAAiRAAiTgjwDdtfzRYVvEEYBXUNWqVWXv3r1RXjsEWOQMW7hwocpnh7xi/gxCn5uVLVvWrYn1LgTI0wUMq+McgR9//FHatGkjyJkM8SJbtmxxbo1ckC+B3bt3m3lKFy9eHJQHHsKu44EYw6sU4d0Zit2XMWvCR+C1115TYiE+x4bB4xIezh07dhR878BnFF7wEPLhITp+/HiL12jhwoVl586dxnC/79WqVZOePXtawu1ev35dCZkIX12vXj0pXry45M2bV/AgAubdvHmzCimsrxEngcfopEmTXP/N/Pzzz/LDDz9Y1pMmTRoZM2aMpU4vNG/eXJDr9bfffjOr8T0OQiu+TyRPntys5wEJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ+CNA4dQfHbZFFAHcMKxRo0a0iKb6xrds2aI8NPT8WXq7cezPywNh7mjBEfAnnJJncCzZO3wEpk6dqm7M3717V50ED1q8/PLL4TshZw6ZAP4foYfftXuzBZoYghA83iCW4v818PqjkUBsEUCoegih+Ewif6hhyO+p5z9NnTq140MBTZs2VeF34dnp1eB1umfPHsHfPd2QXxUvGP5dYA3IL+9kyDkKD/2cOXM6NauHT+ChajfkR0W0CX8GYRV5TRHq3zB8h8O6ESqYRgIkQAIkQAIkQAIkQAIkQAIkQAIkQAJeCDBUrxdK7BPnCeCmIW4eevWcCHZD06ZNk02bNrkOg2iyYcMG13Z6nLqicW2gcOqKhg1xhMCvv/6qwrkaoimWNXfu3DiyOi4DYUmXL18uyOeIv8EI59msWTMZN26cTwhQJ1oIsYtQoggBum/fPjl8+LAKA9+4cWOKpk7AWBfjBODpjIc1MmfO7Hpup1ymEP8hugZr8KqGVyfSGLgZQuu7iaYQS7FefF9zs/bt2/vkQsXDUp07d3YbYtYjxPbw4cPNsnEAIXn16tVGke8kQAIkQAIkQAIkQAIkQAIkQAIkQAIk4JcAPU794mFjJBBAKDaEiPMnXKZIkUIaNWqkbp7nypVL3UCHRwJC++Im3syZM1VoOX/7RU6wsWPHOnbZsWOH8rBwaoSXEm7Y07wTOHLkiJw+fdpxQKZMmSR//vyObawkgZgigL8ZEOHsAsGCBQvk/v37PrkAY2pdCf08EDjnz5+vXosWLZIrV654RgJPuFKlSpnhd8uXL89cyp7psWNsEUA4XnwH+de//qVETX/rgPfp//3f/8l7770X8t8o5GsfNGiQeqgA8/j77mWsBQ8h4O/lp59+KlmyZDGqfd7xUMOsWbMs9cmSJVPerPj36cUQmvenn36yzIOHW5DbFaGDwYBGAiRAAiRAAiRAAiRAAiRAAiRAAiRAAv4IUDj1R4dtEUEAubDgVeRmXbt2FbzcPDJef/11JaBWrFhRDh486DaN35uDefLkkTVr1jiOTZ8+vWM9K90J0NvUnQ1bYp/AnDlzpEmTJgKPRrshxylCQyLXHy38BPAADPKTQiyFty8eugjG8LcbYXfhgffss88KHsygkYBBYNWqVcZhwPdg+rpNdu7cObcmv/XIqzx58mTp3bu3IOcy8pkiFPWNGzfkoYceEnzO8YDZCy+8IBAxDUMuVLxCscqVK8u6desEaQpmzJihvgP9/fffcuHCBZXnGQ+p4VWhQgX14JoXwbJdu3aCV1QND7bQSIAESIAESIAESIAESIAESIAESIAESCBUAhROQyXHcXGGADxB3QzeC23btnVrNutz5MihbrrDw+jMmTNmvX7gT1R94IEHlDer3p/HoROgcBo6O44MLwF4lMJ7HZ7ubgYRj8KpG52o1cPDF38fDK9SHOuhkgPNniZNGqlSpYrpVfr4448HGsJ2EogYAoULF5ZPPvkkxtYL71OEC8aLRgIkQAIkQAIkQAIkQAIkQAIkQAIkQALxhQCF0/hyJRPoPrZv3648K5y2jxxaXkRTY2yBAgWUR8aECROMKsv7zZs3LeX4UIA3yrFjx1RYXHiJwCsXIjJeyZMnj/YtQmyCRx68UuB9gjDGadOm9TkPhVMfJKyIAwTg2fj8888rLy59OfBS1D3FIOrBy50WPQQOHTpkCqUIrY6c1l4Nwg5EbHiU4oXIAuH42+Z1PexHAiRAAiRAAiRAAiRAAiQQGQTsD8vaf0cgLQjyv+/cuVPwmwW/CZFzHamFGjRoIEipgN8jUTWkZfjtt99UhA/cT0Fao5QpU6qUUIUKFVL54xE9J5hoZ3Flb1Flw/EkQAIkQAIkEC4CFE7DRZbzxggBf96mb731VtBrcAvni4nc8pTiy/I777zjeC7k5ho1apTPl+UvvvhC5dpyHBRiZcaMGVXesUDDz549K+PHj5d58+Yp0Rniqd2w7lq1asnLL7+shCJ8KQ/V8KX++++/V1/0V65c6eMd9tRTT6ncbDgXflTAe8xfzrQyZcqEuhSOI4GQCSAceP369eX69euWOZA3D2IqvFANQ5hM9EuVKpVRxfcgCEAYNcLv4u/UgQMHghgtKjSpEX4XYqm/nIpBTczOJEACJEACJEACJEACJEACCYLA0aNH1YPe+mZ3794tECovX74s/fv3lzFjxqg0BXof47hfv37yxBNPyPDhw6V69epGdVDvuH+CPPZu90ewnmXLlslXX32l0jHgvtT7778vGTJk8HueuLA3vwtkIwmQAAmQAAnEAQIUTuPAReASQifw66+/ug5G+NxgrUSJEirfndO4ggULOlXL+vXrBSGBnaxkyZI+oin6DRs2LGgxwGl+vQ4etv4Mgul//vMf9eUeYq8/Q+5G5AjDC6EskUcWIQCDsWvXrsmQIUNk4MCB4u984NeqVSuZNWuWfPvtt7Jnzx65evWq46kefvhhyZo1q2MbK0kgXASQu7BOnTo+n8sWLVrIN998oz7fSZMmFYSRhcE7fenSperhg3CtKT7NazwsAU9dCKXIF22w9LJPCNSVKlVSHqX4O1ikSBEvw9iHBEiABEiABEiABEiABEiABIIiALGyYcOG6r5FoIHwFMUDnRA0P/vsM8d7Q05z3Lt3T95++231EL5Tu1MdxFykbMDv019++SWkVFIxsTentbOOBEiABEiABOIiAQqncfGqcE2eCMBTEiFf3Qw33ytXruzW7FgPr0e8grFgw8pCwAzWg8rLevx5YsJ7C+LkiRMnvExl6bNr1y7B3PjyHUicNQYiHyy88xCyxqshxA28ZkuVKuU6xN8eXQexgQSiQAD/vmvXru0j/jdt2lS+++47SZw4sQqJhBx/8DQ1DCIgvLZpzgSOHDkiyBcLTgi/e/78eeeODrXwTC9atKgZfhd/51OkSOHQk1UkQAIkQAIkQAIkQAIkQAIkED0E8BsGEYf83YdyOhMenEcYXzwoHsjwEHvLli3lp59+cu2K3z4ItXv//n2fPrjnU6VKFZk0aZIlKpJPR1tFTOzNdkoWSYAESIAESCBOE6BwGqcvDxfnj8DJkycdvygaY3r06KHClSDPaThvqgcrnK5bt85YYrS+u4mKgwcPlm7dugmeWgzV4AEKjhBRA+XNWL16tRJNIRAHa2PHjlUirds4tz269Wc9CUSFAEIi4WEBe05NhOX94YcfJEmSJOb0CAlrF07NRh4o4RleuPAohVgKz/JgDLmCEOIK1wOss2XLFsxw9iUBEiABEiABEiABEiABEiCBKBGAoPnPP/+oOfAwJzxP8YA6cpoi4hnER/yG/PzzzwUPk+uGh27x8G3dunX1ap9j3Luxi6Z4WBcP+FeoUEHKlSunQgAjas/x48dVSqQvv/zS8vsKEZCwVoT6RVQ1LxYTe/OyDvYhARIgARIggbhCIHFcWQjXQQLBEgiUtwFfJJHnNE+ePPLGG2+oL5T+QsYGe36j/9q1a41Dn3cnoc+f0OozQRAVTudCPo0PPvggSqKpsQQI1RCj/RnE0iZNmkgooinmhbgLT2E3c9qjW1/Wk0BUCGzevFkJdBcuXLBM06BBA5k8ebIgNK9udm9shGXCv5mEavi3jJsGn376qVSrVk0yZcok9erVkxEjRlh+1LvxQV5lCKUDBgyQTZs2KZbIlYwbExRN3aixngRIgARIgARIgARIgARIIFwEDNEU96IgSk6bNk1eeOEFFQ0nd+7cUrFiRZWTdO/evfLuu+/6LKNLly5+U5IgUhi8U3XDw7oQXfF6/fXXpVixYuoB3uTJk8sjjzyizrNlyxZp3769PkyuX78ujRs3Vp6plgaXQrj35nJaVpMACZAACZBAnCVgvfMbZ5fJhZGAL4HMmTNL6tSpBbk0/dnp06cFT+DhlSxZMpXroWrVqupmPp7WQ368UO2vv/5ST/k5jU+XLp3KD2pvC4dwmjdvXnnwwQctp4K4g1wabgbhB4IG8gFiLMLq/vHHH37FHuTLGDRokCszeKV6EYvSpk0reAUT4gbrRc5YGgmEm8D27dvl2WefVeGU9HMhz+nUqVPV3xG9HscIMY1Q03rIWXhXtmnTxt413pbxxLMRfhfvwT5AgTzKhkcpwktF5W9zvIXMjZEACZAACZAACZAACZAACcQaAdxTmjt3riBVi5tB7EROU6SXGj16tNkNOURxX6pjx45mnX7QqVMnS1Q1nAshd/Fwuj9DhLWvvvpK/X6FmGvYoUOHlOBqF1WNdvt7OPdmPxfLJEACJEACJBDXCdDjNK5fIa7PLwE8QReMIV8Ewmn27dtXeTNB6ICI+sknn6gwtMHMhb7+RNCnnnpK5T+0z4lcngj96eUFb7dXXnnFPoWljC/lffr0sdRBkIS3rZuVL19eNm7cqEJm4gv9v//9b/WFGl5yeGLSzfDUIp6CdLJRo0bJjBkznJpUHULXoA9EqYsXLwpE5zNnzqhz6yFP3SaAwAuhnEYC4SSAcNTwdMRnUzeEh8WPUDzZ62T4DNeoUcPShJC08dnw0ApuGrz33nvqAQw8ZY2HJ/DQhhfRNGvWrPLSSy8JHshALh78/cHfI+SGpWganz853BsJkAAJkAAJkAAJkAAJRCaBdu3a+RVN9V0h+g4e+Nft448/VmlM9Doc4yF2PMyuGwTPQKKp3h+irD210sCBA/Uufo/DtTe/J2UjCZAACZAACcRRAhRO4+iF4bK8EXj//fddhQwvMyD3A/Lu9e7dW+Dt9OSTT6rQkvachm5z+RNO3cLKpkmTRuVehUeqvxeeGkQoFoRkcTOIC7/++qu0bt3a0uXNN9+0eL7pjZUqVZIlS5aocDJ6PY4hbiIcpv3Lvd4PY+0GMRTXws2KFi0q69evF6zriSeeMAVlnAc/Jvr16+c21Kx342l24AEJRJEA8m4+88wzAi913VA3ffr0gLmSIa7qhh+/9+/f16si+hh7QQhj/PiGSIzwu7Vr15ahQ4cq0TPQ5iA640GV/v37q78Hp06dkh9//FF55ebMmTPQcLaTAAmQAAmQAAmQAAmQAAmQQKwRwMOyPXv29Hx+hPS1RwHDA7oIrWu3MWPG2KvU/SCfSj8VWbJkEaSW0W3fvn2CVyAL594CnZvtJEACJEACJBAXCVA4jYtXhWvyTABC58SJE00hzvNAl45bt25VeTwh7v3+++8uvf5XHWx+0/+N9H+EXKzIBwjvVDeDt+zChQulfv36li7r1q1TIo+l8r+Fhx56SH755RfHUKNGf3h1wmvMzeAtajcIoghD42QII4z8H48++qhTs6qDQIzQvf6Mwqk/OmyLKoH9+/cr0dQePrpy5coyc+ZMTx6QCO+rG/LEwLM7kg2e4fgbi9yi2bNnlxIlSki3bt3UE9F48CSQFSpUSN5++23F8Ny5c8pjvXv37iq0caJEiQINZzsJkAAJkAAJkAAJkAAJkAAJxAkCxYsXl1y5cgW1FtzXsduBAwcsVXhAddGiRZY6hALG/a5grWnTpj5Dli1b5lNnrwjX3uznYZkESIAESIAEIoUAhdNIuVJcpysBfDGEF2SxYsVc+wTbgFx9+II7bNgw16H4cgsvSjcrW7asW5PfeoS4RKhQ5Ah0M4TERMhhhNy1G7y/3AyCB8JjBjJ/fRCuV7fDhw+rteh1+vHIkSOVZ61eZz+Gp2ugXJCh8rSfi2USsBNA7hd4ldrz81aoUEE9QOE1RDQeEoBQqFukhevFAxD42/PBBx+oH+rwBEW4cHii2z1x9X0ax/BCffHFF1WOnaNHjwry+Hz++efq7ym87WkkQAIkQAIkQAIkQAIkQAIkEIkEqlSpEvSyIX4++OCDlnF24XTv3r0qP6neqUWLFnrR8zEi/NjN330ro2+49mbMz3cSIAESIAESiDQCSSNtwVwvCTgRQPhZhDvBDf8JEyYo76bLly87dQ2qDuJBxYoVBflK7Yawnk7el+gHsQHencHasWPHBOE+ITa4GUIKI68gxFO7QfiZOnWqvVqVERYXOSu8WDDhRZHP0M0aNWokdevWdWu21OfPn99S1gvwRsW+aSQQ3QQg7lWrVk3wb083CPVz5swJ6Amtj8Ex/v3ib4NhEE7hYRmXDaG2sU68ELrczXvcaQ/JkiUTsMK+8SpdunS0RQBwOh/rSIAESIAESIAESIAESIAESCA2CITyMDei7BQsWNDyECqiHenmFKXIX8Qufaz9GOmg8ODvtWvXzCZEQgpk4dpboPOynQRIgARIgATiKgEKp3H1ynBdIRFAqEy8cOMfHpkIZYuQJ5s2bZI7d+4EPSfGjBgxQr799lufsaHkN/WZRKvYtWuXEh7g7epm8DBF2FB4dTkZ2tz2ef78eb+5S/X53OZAH3toGn/hhBGC16tduHDBtWupUqUoxrjSYUOoBPBvDaLpkSNHLFPgQYl58+YF9JS2DPpvAeIh/mYYtmrVKrl69arEJW9LeI7ib6MhliIcbzCGhxwMoRSeuvhxTiMBEiABEiABEiABEiABEiCB+EwAOURDMfs4u8cp8p7azelBeXsftzI8XBEZzDCkTAlk9jUG6m+028fZ92b04zsJkAAJkAAJRBoBCqeRdsW4Xk8EUqZMKTVq1FAvDIBwgXyky5cvV7n5IGbcvXvX01yzZs1y7Bed+U0xFzwzEabXzZDLFCJlqlSp3LooT1S3xnv37gXlSeY2z8MPP2w2wbNu8+bNZlk/gLgLUcWr+RNOmd/UK0X280oAYiE+nwcPHrQMQW4XCIoZMmSw1HstIDQSvDBv376thty6dUvl9XTKbeN1zqj2wxqQZxj7giCMf7PBeJUjlDYEZoilNWvWFP1vQFTXxvEkQAIkQAIkQAIkQAIkQAIkEAkEMmbMGNIy7Q++2x9cxUPudsuTJ4+9ynMZ69SFUy8RhcK1N8+LZkcSIAESIAESiGMEKJzGsQvC5YSHALy9IJLg9dFHHwlEOoThHDJkiGzYsMHvSfF03s2bNyVFihSWfv48ToMJcwIxAyFtIe66GULsfvnll5IkSRK3LsrT9I8//nBtj64GCEuG+QvT+/zzz0vSpN7/xDj9WDDOEwxPYwzfScCNwKlTp9Tfgn379lm6FC1aVHlihvqjEZMhrDRyoyLvsmH4Nx7Twik82HFevLAWPVSTsS63d/ydwcMKhlcp/v35+9vjNg/rSYAESIAESIAESIAESIAESCC+EAj1d6I9xZP9IV37bzXcR7H3CYahPTRv3rx5Aw4P194CnpgdSIAESIAESCCOEvCuasTRDXBZJBAKAXhQNW/eXL3GjBkjb775pt9p4LGlC6cQUpFT1cmQw8IpJ6pTXwiPr7zyiumd5tSnZ8+e0rdvX6cmSx28P6Mjr6tlUlsBnnTFihUza2fMmGEe2w+efPJJe5Xf8rZt21zb6XHqioYNQRLAj8jq1av75BFGDl08eIBcwFE1CI524TSqcwYaD291rB8epRBL/YX8dpoLXqQIcw6PUvCJyg91p/lZRwIkQAIkQAIkQAIkQAIkQAKRTMCL56bT/o4dO2aptnug2r1LkToJv1uzZs1qGeelgEhjf//9t6XrI488Yik7FcK1N6dzsY4ESIAESIAEIoEAhdNIuEpco0kAnqJunpnwKoUgGqy98cYbSmyYPn2641DMac/hh1CXRihO+6DHHntM0qdPb6/2KY8cOZYNf78AACAASURBVFI6d+4s+GLrZIkTJ5bhw4dLp06dnJp96vbv3+9TZ1Q8/vjjEh1em9mzZ7cIyBBr3eyhhx5ya/KpP3TokKvnL84ZlfwePidjRYIlAHERIbx37NhhYVCoUCGVCzmUH6aWif5bgHDao0cPswn/TvBjOTo/x/j7s3r1atOrFJ7zbn9LzIVoB/ibZoTfxXoLFCigtfKQBEiABEiABEiABEiABEiABEhAJ+CUi1RvdzsOJJw6/RY7cuRISMIpoitBeNXNi3Aarr3p6+AxCZAACZAACUQSAQqnkXS1uFblGeoWHhbhbL/++uuQKOXKlct1HEQVu0U1v+nHH38sffr0sU9rlpMnTy4TJ06Upk2bmnWBDvwJpxBGhg0bFmiKoNqRl8NNxMZEOXPm9Dzf1KlTXfvS29QVDRuCIIBQ0PCo3Lp1q2VU/vz5lWiaLVs2S31UCiVKlJAsWbKI/uMTnqDt27ePyrSC0MKGR+nixYvlypUrnufDgxilSpVSHqX4e1CuXLmgQml7PhE7kgAJkAAJkAAJkAAJkAAJkEA8JGD35PSyRTzwCjFTN/sDtfhNajcIp14jmeljnR5u9yKchmtv+tp4TAIkQAIkQAKRRIDCaSRdLa5Vjh496koB4XNDtTVr1rgOrVOnjk9bqPlN4RH29ttvy6hRo3zmNCrgrfrrr7+qHIxGnZd3f6E5IZpEt50+fTpapkRImK+++sp1rujwlHWdnA0JggByykAs3LRpk2W/+AEJATIYkd8ygUsB/97g2ao/5IHwucEKp/CwR/hdjIVgih/PwRhCPkEsxt6xHntIqGDmYl8SIAESIAESIAESIAESIAESSMgEli5dKk2aNAkKwfr1630iA1WqVMkyB3KQ4uF5pIgybN26ddK4cWOj6Pnd6aF0L8JpuPbmeeHsSAIkQAIkQAJxjED0qylxbINcTvwi4O8puJ07d4a0WYToxZdZN3vhhRd8mvwJp24ekvgSjLyq/kRThKXFF9ZnnnnG55yBKvBF283CIZwi36k/88dUH4ccrv68Zd146nPwmATcCCDvb61atXz+jePH6aJFi8Sft7nbnF7qIVbqBgE0UChdhFRauXKlfPTRR8ojFF6r+GE+duxYT6IpwpXXrVtXhfjetWuXGgMvfHiuUzTVrwaPSYAESIAESIAESIAESIAESCA4ArNnzw5ugIgMGDDAMiZRokRSpUoVSx3u19jFTfyOu379uqVfoAJ+T9qF04IFC3pKGROuvQVaM9tJgARIgARIIK4SoHAaV68M1+VIwN/N/40bN4q/ELpOE8Jr8sMPP3RqUnUQXIoWLWppR8hPhMx0spQpU0qxYsV8mhBSs169evLTTz/5tBkVCM8C0aR48eJGVVDvqVOndu2/YMECnzwX9s53796V1q1bq/NjDfbX+PHjLUNSpEhhKdsLq1atslf5lJcsWeI3hDB+VJQuXdpnHCtIwAsB/LurXbu22D3KIZbC0xTiabjMLpyeO3fOR7zFuQ8ePChjxoyRRo0aqfC+FStWlP/7//buBO6Gsv/j+E9ZQtYQicgWJVuLnrI9CO2heLQv6kFJT5vq0a7SplUbRSXte1EUSaksJSRpsWSLKLIX/9d3nv85r5k5c7Z7OWfO7XO9Xud1z1xzzcw177m7G+c31++65Ranz/pvMlHRfx8tW7a0wYMHO0FgneOdd95x5k7WXMsUBBBAAAEEEEAAAQQQQACBghHQv93efvvtlA82f/58e+uttzzt9f1S1apVPXVa6dWrl6fut99+c6Zv8lQmWdH3PmvWrPG00ovqqbxIX5jX5ukQKwgggAACCOSIAIHTHLlRdPN/AslGh3Xo0MFGjx6dlEsBiQcffND09p0eZoOKHi7vvPPOmE2JRpsq2Ogfial5DjWCVA+x8YrmrlDQ1P+WYbz2QfU1atQIqnbqNK/jww8/HHf7rl27nDSizzzzjM2ZMyfms2zZMiew4z6Agk4a4RavKEj8/PPPx9vs9EeB6USj8HR/KlSoEPcYbEAgnsDmzZudlxX035W7KC2vgqZ169Z1Vxf4cs2aNe3ggw/2HFfpdjds2GAa5d6/f3/TyxL16tVz5m5Wem6lFE5W1P9zzz3X+W9LL37MmjXL7rjjDtPfvkSjzpMdl+0IIIAAAggggAACCCCAAAKJBf7zn/+Y/q2ZrGgqqauuusr0XYu7XHHFFe7V6LLa+r/T0WjVFStWRNskWlCGM71Q6y76t2afPn3cVQmXC+vaEp6UjQgggAACCIRUgDlOQ3pj6FawgAJtCjrEK0plct555znBU438VOCtUaNGptGYCkqsXr3aGfWlIGa8UaORY990003WrFmzyGr0Z6LAqX8+Tj1Qa/6K7777Lrp/0IJGqioYkk7R6NvnnnsuukuXLl2iy0ELQ4YMcar79etnkdGieohXamC9hZhohOjNN98cE8AsXry4MxpUo0aDio6te6EUyhpFp5FxmqNWKXx1DxVESlb8nsnasx0BCejvwIknnuj8brtFlApb6XkVsMxE0ahT94sZ99xzjzOaVCmUUi2lS5e2tm3bOvOU6niHHHJIqrvSDgEEENjtBJTaXNkGIkUvpulvKAUBBBBAAAEEECgIAU0z1LlzZyfTT6VKlQIPqVGfmvLJ/xKvvps644wzAvfRS+m33nqr80J7pIFGgbZu3drGjx8f81JupI1+Kkjbt29f0wvz7nLdddeZvrdJtRTWtaV6ftohgAACCCAQJoHU/w8apl7Tl91WQG/LXXnllZ4vxYIwFAzUJ6/ltNNOi5vCN1Hg1D8fp0aDJQuaqo/Tpk1Lu6v+QKlGgCrQqxGjQUVfJF5++eV2zTXXWJ06dUzBWgWPk82bMXDgQLvkkkuCDumkk4kXONUOeutx6NChgfumUun3TGUf2uzeAvpH4ymnnOIESN0S1apVM80zqn+sZqoo0Dl8+PDo6TTaNFlR+l299KF99dGLF5EXHZLty3YEEEBgdxfQi17ukRl6ZiRwurv/VnD9CCCAAAIIFKyAXjpv3LixkzmoZ8+ezvcre+65p/OSuIKlmopl8eLFnpMqo9m9995rahev6MXzBx54wObOnRttouxfehFdo0n1/YheSI9k5dJ3PK+99prdd999Md8DKZvZ2WefHT1OqguFdW2pnp92CCCAAAIIhEWAwGlY7gT9SEmgXLlyzkPhRRddlFL7vDTSw+WTTz5pCmAElXQCp+nOuRp0vnh1QXN/PvTQQ9a+ffuE6W8VzPz+++/jHdZTr7ch77//fk+de0UP9pqPceXKle7qlJeV1rhBgwbOqNSgnQicBqlQF09Av9uaK/SDDz7wNNlnn31s0qRJ1qRJE099Ya/oy3r9HfGnZ/Kfd99993XeWtbLEHp7WesUBBBAAAEEEEAAAQQQQACB8Aicf/759vTTTzv/vlM2M2Up00dFAVFNCRWvPPLII3b88cfH2+zUK7g6atQo5wXa33//PdpWy5E0vPr3Ze3atZ3BBJoHNajopeF33nknrdGmhX1tQf2kDgEEEEAAgTALMMdpmO8OfQsUUAoSzQ9Y0EVBvNtuu83GjBkTd65AvTWoeQWDilLn+lOAJgqyBh0jnbqgoKJGp/33v/9N5zCBbfUwrrQusogXQNaOGgk3YsSIhG0CT2DmPMS/8MILFi+9jY4dlCo53vGo370FduzYYRop/t5773kg9PuloGnTpk099ZlYUYrwKlWqxJxKv9udOnWyu+66y77++mvnxYNnn33WzjzzTIKmMVpUIIAAAggggAACCCCAAALZF+jVq5cpAKoAp7/EC5oq05eyEP373//27xK4rhfkldVL08wEFb2Uu2TJEosXNFUmMk1NpRGx6ZRMXFs6/aEtAggggAAC2RaI/b99tnvE+RFIQUAPq0p/oofQgij//Oc/nQCG5vpMVBIFQoMCmYnaJzpPKtuCRpxqP6Wpe/jhh9N6u9B9vv3339/effddJ8VuojQykX2UFlUB53SK3oB888037aSTTrLZs2cH7tq8efO4AezAHajcbQU0Z2jv3r3trbfe8hgohZH+0ajfpWyViy++2HPqGjVq2Pr1651+XXXVVc7LAYleTvDszAoCCCCAAAIIIIAAAggggEDWBPr16+d8X5LKFDAKRmrqpkGDBqXVX71AvmDBAmc/veCfSildurQNGTLE2U9Tv+SlZOLa8tIv9kEAAQQQQCAbAqTqzYY65ywQAQUklNZS6WmVLuWPP/5I67hly5a1Hj16mFKStGvXLqV9EwVC/YFTjUzVm4CFUWrVqhX3DUSdb8CAAc7ciBp9+vbbb6fUBc2BcfXVV5vS75YsWTKlfSKNNDpVbzQqhfLatWsj1TE/9dCvAJfeuFT6VI22izfHqt8z5mBUIGDmpEPSSE3N7eIu5cuXt/fff99atWrlrs74sv7GuF8sUFrrVatWWd26dTPeF06IAAIIIIAAAggggAACCCCQP4GuXbs6AUr9e/ONN95wvvdZunSpVaxY0ZkeRlPEaNqW/PxbVMfS9yZK0asX28ePH28//fSTKUXwtm3brGrVqs5HAVylAFZGI33Hld+SiWvLbx/ZHwEEEEAAgUwIEDjNhDLnKDQBBfv0MDl06FDTJPYKbM6YMcMJTCiQqrkglEZFD5367Lfffs7Dq0ZrKjC39957p9W3e+65x/RJpWhUZbK5DVM5Tl7b6C1DjcBbtGiRTZgwwaZOneqkBF2zZo2TYlcP2ppL8cgjj3Qesg8++OC8nsrZ79RTT7Vu3bo559RD/bJly5yRdXKQu87Ts2dPU0rjSNFIwGwaRfrBz9wU2Llzp51zzjn24osvei5A/13rd1C/c9kueltY/w24U3xrDlb/SNRs95PzI4AAAggggAACCCCAAAIIpCagrEEKMupTmEXf2ehlf30yVTJ1bZm6Hs6DAAIIIIBAXgQInOZFjX1CJ6C5BPWGnT4Ur0CDBg1Mn0svvdS7oRDWlDr59NNPdz6FcHgOiUBUQAF3/eNx7Nix0Tot6G+B3sj9xz/+4anP1or+0Xnsscfac889F+2C3kwmcBrlYAGBnBLQCxt6UUuj3OfNm+e8kLR582bTfFJ6matevXp21FFHWfv27fN1XfPnz3dS2n/++efOyAK9fKH/x+pFDI0s6NKli5N1Q6Pr0yk///yzKb15pGh/fSGXatm6davzYpS7va49XqYKvUSlfSJF/VcadXfR6AmN1vjxxx9txYoVzrzpSmtep04d03QAeoaJV/SCnF4IixT3talOqdH1Almk6P8RNWvWjKzyEwEEEEAAAQQQQAABBBBAAAEEEIgRIHAaQ0IFAggggECYBRQ0VVroMWPGeLqpeV3eeecdJy2SZ0OWV/yB048++shJMZzKHMJZ7jqnRwABl4D+vmiOKgX4/EXBv8mTJ0erjznmGGfOcc2hnk759NNP7bLLLrNZs2YF7qZ5spRB4sknn7Ry5co5/bniiitigpGBO5uZ+rN48eLoZr2AMmrUqOh6soWvvvoq5sUUBZDjZa3Q3F7Tp0+PHvaJJ56wvn37OuszZ860gQMHerZHG/7/gqYQOProo50+Bs0lNnr0aLv88sv9u0XXdW3u61PAWVk4KAgggAACCCCAAAIIIIAAAggggEA8gT3ibaAeAQQQQACBMApoDt+RI0d6uqaRWG+++aZ16NDBUx+GFc3F7C5KI/7FF1+4q1hGAIEQC2zfvt369OljJ554YmDQNKjr06ZNs44dOzr7BW3312kkq/62KeAaL2jq32fjxo126623OkHLXPqbopdfNAd769atEwZNI9erYLLmCFOacwoCCCCAAAIIIIAAAggggAACCCBQ2AKMOC1sYY6PAAIIIFAgAkpxe+ONN3qCjprDWAGNG264IWE6xwLpQB4PUr16ddMcwK+//nr0CI8++mjMqK3oRhYQQCA0Aq+++qoNHjzYfvjhh2iflIJbox8VzKtSpYqTQnfdunX222+/OfOsKz1tpIwbN85JF6uRlrVq1YpUe36+9957ppGVSs8bKTpH06ZNrXbt2rb//vubUtcqDa1evFi4cKFptKbOp7J8+XJnVGbv3r3t7rvvdtpGjhO2n0pzrBdfNCe9SokSJUxzsitdsFL4KoC8dOlSU0rhVatWRbu/adMm6969u2mEqeZLjxSlCdYo0kiZMmWKbdu2LbLqpPt1j1TVPaMggAACCCCAAAIIIIAAAggggAACiQQInCbSYRsCCCCAQCgEFLgYNmyYpy/Fixe3l156yQlKejaEcKVfv36ewKmCKQ899JBVrFgxhL2lSwggIIG33nrLE6RTnebI1EsQSsEdVBT4U/BSf7MiRalhe/ToEQ0WRur1U+l9TzjhBNMozEhRGm+lIj/jjDMiVTE/FRy85JJLoqPv//77b2fOZwUmlc433pyjMQfKcIUCn5GiOdEfeeQRJ/gcqYv8lONpp53mzCUbqVPwVGmJ3YFTvZSiT6Ro/lLNkxopaqv7QUEAAQQQQAABBBBAAAEEEEAAAQRSFSBVb6pStEMAAQQQyIrAkCFDYoKmCiyMHTvW84V5VjqX4knbtGljmoM1UhTk0FynFAQQCKeAAndBc2dqBGq8oKmuRKPgr7nmGidQ6r6y2bNnO6NF3XVaVvDTHTTVCMwXXnghYdBU+5UqVcqZ51SjMN1FIzWfeeYZd1UolzVPtV4g0YjdoCLH559/3o444gjPZo1GnTNnjqeOFQQQQAABBBBAAAEEEEAAAQQQQKAgBQicFqQmx0IAAQQQKFCBW265xW677baYYz755JOm0Uq5UjQHq4Kn7sJ8fW4NlhEIl8D48ePtp59+8nRKoxe7du3qqYu38sADDzhB1Mh2vSyheU/d5cMPP7Rvv/3WXWUXXnihZ0SlZ2PAyuOPP27ly5f3bLnrrrs862FbUdrh4cOHe3yC+qjgsEbr+4s7bbJ/G+sIIIAAAggggAACCCCAAAIIIIBAfgVyPlXv1KlTbcCAAfl1YH8EEEAAgSwK6G+5v9x5553OnKb+eq3r775GZXXu3Nk6depkzZo1M80JGOaiefjcwVL3cpj7Td8Q2B0F9HKGv1x33XX+qrjrShl75JFH2vTp06NtNP/m8ccfH11/7LHHosuRhYsvvjiymNJPjdg86aST7Lnnnou2X7RokenToEGDaF2YFjQntVIep1L0991f1q5d669iHQEEEEAAAQQQKFCBypUrO1OruA/auHFj92rOLhfla8vZm0LHEUAAAQRCJ5BzgdNly5Z5EOfNm2f6UBBAAAEEio6AAgz+kVjuq9uyZYsThIwEHxU86NixYzSQesABB7ibh2LZn95TKTXDHNwIBRqdQCALAkqd63+ZQ4HQFi1apNWbgQMH2oEHHhjdp1KlStFlncOfrrt169bOSyDRRikuaPS9O3Cq3dT/MAZOZaBRtamW6tWrOy/FuNMZ//bbb6nuTjsEEEAAAQQQQCBPAnvvvbczpUKedg75TkX52kJOT/cQQAABBHJIIOcCp8uXL88hXrqKAAIIIJAXAc0vePfdd9ukSZPsk08+sc2bNyc8jEYgvfjii85HDevXr++MRNWI1A4dOpg7YJHwQIW48ZBDDjGlqFy5cmX0LAr8hjG4Ee0gCwjshgLff/+9rV+/3nPl7pGing0JVnr37m36BBWdY926dZ5Nffr08aynutK+ffuYpjNnzrQLLrggpj7bFY0aNbLixVP/54falitXzjZs2BDtuns5WskCAggggAACCCCAAAIIIIAAAgggUEACOTfHqd74pyCAAAIIFG0BBROvvPJKmzBhghNc0Mgspck8/PDDk86LJxnNgac0mD169DCNRj3iiCOc/SdPnmzbtm3LGp5/1GlkxGzWOsSJEUAgRuCrr76KqdPfkIIss2fPjjlcvXr1YupSqVBg0Z/6ds2aNansmvE2enkk3bLnnnt6dnGPPvVsYAUBBBBAAAEEEEAAAQQQQAABBBAoAIHUX/kugJMVxCFq1arlOYxy8++3336eOlYQQAABBHJLYMWKFZ7RV+6/9aVKlXJGjWrk6NChQ+333393UlxqNOrEiROdIGmiq9Xo1RkzZjifO+64w0qXLm1t2rSJjkjN5PyoCpyOGTMm2l0Fcv/666+0RmBFd2YBAQQKRSAoFWzVqlUL9FxB83S6/+6le7Jq1arZ4sWLo7v5R7NGN2R5QX9/KQgggAACCCCAAAIIIIAAAggggECYBXIucOrHVAq0Rx55xF/NOgIIIIBADgkMGDDARowYkVKPK1asaN27d3c+2mHJkiVOSl8FUj/88ENLNtIq3vyonTp1coKpderUSakfeWmk1MHFihWzyIipjRs32vTp051Abl6Oxz4IIFDwAv40vTpDQQdOg85Ru3btPF+M0pG7A6dbt27N87ES7Rj525WoTaJtehGGggACCCCAAAIIIIAAAggggAACCIRZIOdS9YYZk74hgAACCGRe4IADDnDm8hs3bpytXr3alGZT86N26dIlJn1lUO8i86P27dvX6tat68w52q9fP3v11Vdj5jkM2j+dOgVfmjdv7tmFdL0eDlYQyLqAXmjwF2U4Kcjin7dZc3lWqFAhz6fwvzCiv4uFUYJs0jmPXhyhIIAAAggggAACCCCAAAIIIIAAAmEWIHAa5rtD3xBAAAEE0hLQl/IKTAbNj6o5Cv1z5QUdPDI/as+ePT3zo2qe1YKYH5V5ToPUqUMgPAI1a9aM6UzQCNGYRmlU+EeXKmW3P/iZ6uGUjnzVqlWe5gceeKBnvaBWCtqhoPrFcRBAAAEEEEAAAQQQQAABBBBAAIGCEiBwWlCSHAcBBBBAIHQCkflRNTfqF198YRpdqpGkGlHaoEGDpP2NzI+quVE7duxoSoepkawa0aqRrXlJW6n93WXmzJme+V3d21hGAIHMCzRs2DDmpMuWLYupy09F0N8fpR3PS9FIewVe3aWwAqeaY5qCAAIIIIAAAggggAACCCCAAAIIFGWBnJ/jtCjfHK4NAQQQQKBgBfzzoy5dutQmTpzozJGaqflRjz76aCeFcCRVp4Kzmp/19NNPL9iL5WgIIJAngaCgpv5WpFsUCP3555+ju5UrV85atWrlrNevXz9aH1lQ+8MOOyyymvLPhQsXxrQtrMDpokWLYs5FBQIIIIAAAggggAACCCCAAAIIIFCUBBhxWpTuJteCAAIIIJCWgNJlXnDBBZbJ+VFLlixp7du39/STeU49HKwgkFWBOnXqmP47dZc5c+a4V1NaHjhwoHXo0CH6GTFiRHQ/zUHqP8eMGTOi29NZePnll2Oaxwuc+ucYTXcEqVKWUxBAAAEEEEAAAQQQQAABBBBAAIGiLEDgtCjfXa4NAQQQQCBlAf/8qJrLT0GC6667zgp6flTmOU35ttAQgYwLaC5k/4jQV155Ja2U2hs3brT333/f0/du3bpF1/fYYw/zBzdHjhxpW7ZsibZJZUEpev2BU6UarlWrVuDu++yzj6deKcdTLb/++qvlJYCc6vFphwACCCCAAAIIIIAAAggggAACCIRBgMBpGO4CfUAAAQQQCJ2ARoNptFhhzI9atWpVz/Vq/sTvvvvOU8cKAghkT+CSSy7xnFwBzaefftpTl2hl9OjRtm3btmiT4sWLW+fOnaPrWujVq5dn/bfffrNnn33WU5dsRanG16xZ42l2/fXXmwKzQWXffff1VCuVcKqjTm+//fY8zevsOSErCCCAAAIIIIAAAggggAACCCCAQMgFgr9VCXmn6V7hCejL+3r16nk+06dPL7wTcmQEEEAgRwQi86Mq3eb3339vmo9QI8R69+5t/kBo0CUp8KKUvFdffbWdccYZMU1I1xtDQgUCWRPo27ev+ec6HT58uC1evDhpn3788Ue79tprPe2OOuooq1Chgqfuqquusho1anjqhg0bZitWrPDUxVvZvn27DR482LNZz3B9+vTx1LlX9t9/f/eqs/zuu+/G1PkrlEbYnWrYvz0s6/5UxDKiIIAAAggggAACCCCAAAIIIIAAAukIEDhNR2s3aPvFF1/YTz/9FP1oJMJBBx20G1w5l4gAAgikJ+CfH/Xrr7+2u+++27p06WJlypRJ72BmTlA17Z3YAQEECkVAI0TvuOMOz7GXL19ubdq0STg6XC9V9OzZ0zZt2uTZ1z+CVRvLli1rt956q6ednsFat25t8+fP99T7VzSa9cILL7RvvvnGs0mpxdX3eOXEE0+M2aTjTJ48OaY+UqGXOjp27Gg7duyIVIX2Z7Vq1Tx9+/zzz23nzp2eOlYQQAABBBBAAAEEEEAAAQQQQACBRALxv1lJtBfbiqyAAqfuotEWlSpVclexjAACCCDgE9Aop2bNmjmfK6+80jTK6bPPPjOl0Zw0aZLNmjXL/v77b99e3tUpU6Y4+ylFMAUBBLIv0KNHDzvyyCPN/Wz0yy+/OMHTc845xwlwanupUqWcQOfUqVNNI0b985T269fPTj/99MALOu+88+yBBx6wuXPnRrcr+8cxxxzjjCbV/MotW7aMjlb9888/7bXXXrP77rsvZr5RzZl69tlnR48TtKD5lTXPqdICR8rWrVtNAdXu3bs716vzbdiwwXmJbuzYsfbpp59GmjrpyzX6VP0IY9ELLe55W7/88kvr1KmTtW3b1hndq8DqqaeeGsau0ycEEEAAAQQQQAABBBBAAAEEEAiJAIHTkNyIsHRDXzC5i76woyCAAAIIpCeg4Gf79u2dj+ZI1RyCGtEVCaQuWrQo5oAaoaYAheZVpSCAQDgExo0b5/x3vHTp0miH1q5da/fee290PdHC4Ycfbvfff3/cJpqLdNSoUaaApnuuUS1H0vDqxQwFBBWsdAc83QdVQPCdd95JONpU7UuUKGH33HOPKWDrLvr7o/lVE82x2rBhQ3v11VftgAMOcO8aquWuXbvam2++6emT/vZGRtQqIwCBUw8PKwgggAACCCCAAAIIIIAAAggg4BMgVa8PlHkU5AAAIABJREFUZHde1WgojYpyFwKnbg2WEUAAgbwJaH5UfVnvnh9VwRL//KjMc5o3X/ZCoLAE6tatax9//LE1btw47VN069bNCTQmG0Wu4KpGnFevXj3wHLt27XLmVI4XNFUgUy9lpNrHc8891wYOHBh4rniVderUcQKzykLy119/xWuW9XqNBN53332z3g86gAACCCCAAAIIIIAAAggggAACuStA4DR3712B91zzafnn5FIKOgoCCCCAQMEKaPTY+eefbxrNtnr1aovMj8pcfAXrzNEQKAgBBQ313+jtt99uegkiWdFLZxrh+N5771mtWrWSNXe2K9X3ggULbNCgQc6o0FR2Kl26tA0ZMsTZ79BDD01ll2gbpQd++eWXk/ZP16u5m7/77jvT9A0qydKOR0+ShQWZaF5Tnl+zgM8pEUAAAQQQQAABBBBAAAEEECgiAqTqLSI3siAuw5+mV3N26Ys8CgIIIIBA4Qm450ctvLNwZAQQyI+ARo1ee+21pjmMP/zwQ/voo49sxYoV9uuvvzojHBVU1KdRo0bOnKR5OZeClMOHD3dS9L777rs2fvx4Z55RvVyxbds2q1q1qvPROY4//nhn7s6yZcvm5VTOPj179jTN46o5XDUX88qVK50XOTSq9KCDDnKu5eijj46Z6159SbVoruf8lnXr1qV1CAW6dV6lV164cKFpztgyZcrY/vvvby1atEjrWDRGAAEEEEAAAQQQQAABBBBAAIHdT4DA6e53z+NesT9wqtELCp5SEEAAAQQQQAABBP43R6jm0dSnsIpSzWpEuj6FXfTiRuvWrZ1PYZ8rk8fX3LEKoOpDQQABBBBAAAEEEEAAAQQQQAABBNIRIHCajlYRb6sRB+5CmjO3BssIIIAAAggggAACCCCAAAIIIIAAAruDwKpVq2zjxo3RS61cubLts88+0XVNX/DOO+/YzJkzbe7cuaYMJaNHj3YyXUQbxVn45ptvbMKECc489jqPpmzRdC6au75Lly528MEHx9kzfrWybGzdujXaQNlMlLHEXT755BP79NNPbdGiRbZ27VrnnPXq1bPOnTsnPOfmzZvtzTfftFmzZtkvv/ziTPNVvXp1Z/+TTjqpQLLVafownUPTLijjijK77LXXXlatWjUnE4pc1M/y5cu7LylwWZlh/FOR6eXEVPb1H3DDhg1Of9z1ck1lCg/tUxj32t0XlhFAAAEECkeAwGnhuObcUfVAoYcUd9EcXemWP//800n3ptGrkQcdPZjojf8mTZrYKaecYummlduyZYvzYBbpy5577mkHHnhgZNX5uWPHDidtnh4Cly9f7jxg6SGmRo0adthhhznn1QMXBQEEEEAAAQQQQAABBBBAAAEEEEAAgUQCl19+ub3wwgvRJtdff73ddtttzvq8efOc7CAzZsyIbtfCE088ETdwquDo008/bbfffrszHYNnR9fKFVdcYa1atbKLL77YLrjgAlMmjVRKr169bPr06dGmAwcONM1rr6JpGW644QbP9mhDM+ccvXv3dvqm4G2kbN++3dnvkUceMX3fF1R03JYtW9ozzzyTMPgatK/qFMi97LLLnKBsUJvvvvvOpk6dak8++aSVK1fOBg0aZDKqUKFCUHOnbtSoUU6/3Q369+9vuo50y5AhQ+zBBx/07KbgbqLBJoV9rz2dYQUBBBBAoFAECJwWCmvuHXT27Nmmt+XcJZ3Aqfa/5ZZbnPm49GAVryiIesYZZ9jNN98c8+ZbvH1eeuklO/fcc6Ob9UCmt9xU1q9f7xxLb/X98ccf0Tb+BT1Qad6w8847z7+JdQQQQAABBBBAAAEEEEAAAQQQQAABBJIKvP3226a54hN99+U/yI8//mgKbEa+y/Jv96+r3UUXXeSMYFUQUPPP57UoUPvf//7Xdu3aFfcQCvQ9//zzToBSAxI0+GHx4sV28sknOyMm4+74/xv0naC+Q9SI0U6dOiVr7mzXOS+99FIbMWJESu3VSCOAb731Vnvqqafs1VdfjRu8PPPMM+3GG2/0XPNrr71mDz30UMqBaJ1PfdR3ku7SuHHjuOdVu2zea3c/WUYAAQQQyJ9Aaq8t5e8c7J0DAv40vZUqVbKGDRsm7blSe5x++unOqE49ICV7cFSKi0cffdRatGhh06ZNS3p8NfDPvRoJ6OpBqUGDBs4bdImCpjqGtmuusHQeyFLqHI0QQAABBBBAAAEEEEAAAQQQQAABBIq8wMcff+x8B5bsuy83hEanHnPMMSkHTd37fvbZZ85oTv3MSxk6dKhppGyioKn7uErDe/bZZ9u6deusW7duKQVNI/srna/2/e233yJVcX8qa9y//vWvhN/RlSpVyooVKxZ4DGWaa9eunSkYGlTq1q1rbdu29WxSSmSNbk2n6H5rP3dxD+xw12s5m/fa3xfWEUAAAQTyJ8CI0/z5FZm9/cFJpbeN94ASuWjto7fsNI9CukUPOf/85z9NAVsFURMVf1BXI06VckRviqVblEJED3J77713urvSHgEEEEAAAQQQQAABBBBAAAEEEEBgNxTQQAAF+9zziEYYlF3Nn8VN2/TdV/v27WOCiRqscM455zgpeZs3b+4EKpX+VR+N+NQghUjR9FWaR1RBv0aNGkWqk/589913ndGPkYYaRaoRpPq+T/O1fvXVV07WOH8wUedXn9zf9WmeUe176KGHWq1atZy5WTW/68SJEyOHd36uXLnS7r//fmdUqGeDb+Waa66JGcmplMTKUHf00UfbUUcd5aT9lamCuRqo8fjjj9vChQujR9q2bZtpZKn6H/S9or77U+DTXV555RVr06aNuyrhsjtVsxpq6rCzzjorcJ9s3uvADlGJAAIIIJAvAQKn+eIrOjv7A6eRUZ3xrlDzC2hSdv9bdocccogzn6gesjSZvVJ76CEm8nG31xtmelCcOXOmlSxZMvBUeiDVROrucu+993oelvTwp7kYNO+p5mJYs2aN83Ckh0RNCO8uevPt/ffftx49erirWUYAAQQQQAABBBBAAAEEEEAAAQQQQCBQQClzNaoyUvr27et8/6Vg4v777x+pjv7UKM+gEZgKpGo+UAUg3SUyQnL16tXOQINvv/02ulnfZWm+01Qzt2lHpYxV0aCIfv362V133WVly5aNHvO4446zwYMHO9/LjR07NlqvBXfQVNnb7r77bifY6m6kAQ0KRJ522mnuamcUqNLpxiuTJ092gqvu7QpIjhkzxgmc+uv1XZ/mm9UcpZdccomNHDky2kRBZX2/p3lQ/d8raqCH2qtNpGiEqgK7yQaKqP1ff/3lpAOO7KufXbp0sRo1arirnOVs3+uYDlGBAAIIIJBvAVL15psw9w+gtBNLlizxXEiiwKneINPcDO4gqB6+7rnnHueNNT0g6cFFczB07drVedPso48+8jzcRE42d+5ce++99yKrMT+//vprU4DVXSJvmOkNPc3B8MMPP9gdd9xhemg99thjnQetJ554wj788EMrXbq0e1dnWW+rURBAAAEEEEAAAQQQQAABBBBAAAEEEEhFIBI0rVixohMc1PdOCj4GBU11PAVa9V2Yu2jEqr6r8gdN3W323XdfmzJlimlggrtoQIIGAqRb9H3ZI4884gmaRo6hgOXo0aNN368FFQVcdR0aoRpUFJw877zzPJsU8F26dKmnzr2iYKY7dXCJEiVMIzs12jRRUereJ5980rp37+5p9vPPPzuBaE+lmWkU8Kmnnuqp1veBGtWbSpk0aVLMSOF4aXrDcq9TuS7aIIAAAgikJkDgNDWnIt1qxowZMdd35JFHxtSpQm9caU5Td47/2rVrO3n8r7jiCitePP4gZqWz0Btp/vL222/7q6Lr/pGwkQ16o0+Tz+uhUw96QUWB25tuuilmkwK/FAQQQAABBBBAAAEEEEAAAQQQQAABBNIR0KhFf0DOv78Cg8qW5i5VqlRxppxSStpkpWrVqqbA3V577eVp+uijj3rWk61otOagQYMSNtP3eBqE4C/qg+ZITVY0DZe/uEesurcpaOweSattF154oTMNmLtdomWl7FVQ1F00mjaoaMSvv2iUbCrFn6ZXwWOlTPaXsNxrf79YRwABBBDIn0D8KFf+jsveOSTgD04qEKo33ILKVVdd5UkNorlCFfhUutxUih4cx40b56TTjbRXqt54xT+/qdrp7b433ngjpXP+4x//iDl0vDflYhpSgQACCCCAAAKhF9D0AQMGDAh9P+kgAggggECwgP6OUxBAAIFcEFB2tQ4dOiTtqkaGKn2suyjd7T777OOuSris7+WUBvfZZ5+NtlOaWw1oSDRoIdrYzO68807TSM1kJWgErAY+xBuJ6j5ew4YN3avOsqbQCiqPPfZYTPXFF18cU5eoQgFoBTCfe+65aLNFixaZPg0aNIjWaaFTp0623377eabxevXVV2OC2p6dzEzzp+p7R3fp06dPoGVY7rW7rywjgAACCORfgMBp/g1z/gj+wGm8NL16uNBcAO6iIKhGf6Za9HCnSds/+OCD6C4bNmyILvsX/H3Tds1nULduXX/TwPVy5crF1OuNOwoCCCCAAAIIFA2BefPmOZkvisbVcBUIIIAAAggggAACYRW44YYbUuqaf0oqzanpTzGbyoE0TZY7cKrvz5R9Ld73dv5jduvWzV8VuB40wEBZ3FIpQd+7aU5Wf9HITH/q4tatW1uzZs38TZOuKxOeO3CqHfQSjj9wqgx1SgGsoHWkaKoyZd47/PDDI1UxPydMmGB//PGHpz5emt6w3GtPZ1lBAAEEEMi3QPL8EPk+BQcIs4AeXPzByXhpevWmmrto4voTTjjBXZXSsj/oGZknwr/zunXrnPlL3fV6aNQbfqkW/9yt2o/Aaap6tEMAAQQQQCB8Aqm8NR++XtMjBBBAAIFUBfg7n6oU7RBAIJMC+tt08MEHp3TKTz75xNOufv36MellPQ3irAQNVPj+++/jtPZWa7SossSlUvwpgbVPfr4706hYf1G/9T2fu2gUZ15K+/btY3aLl80uL+l6/Wl6mzZtaq1atYo5pyrCcK8DO0YlAggggEC+BBhxmi++3N9ZqSx+//13z4UEvbmmFCP+h5D+/ft79kt1ZcuWLZ6mFSpU8KxHVoLmXj3//PMjm1P6+cMPP3jaaS6JoDQinkasIIAAAggggEBoBfTm/PDhw0PbPzqGAAIIIJA/gVRHSOXvLOyNAAIIpCfQqFEj0wjGZEVpXr/55htPM01vtWDBAk9dqitlypQx94CDxYsXp7TrAQcckFK7eI3yEzgNOqZGyvpLvXr1/FUprWuUq98lXnrgQw45xFq2bOmM1I0cXBn1hg0bFln1/JS1piRzl3ijTcNyr919ZRkBBBBAoGAECJwWjGPOHsU/2lQPgUFvUblTg+hi9ZCitB15efBbvXq1x6tatWqe9ciKf35TvSkXNOl8pH3QT3+wt3Hjxim/cRd0POoQQAABBBBAILsCnTt3dlL+jx8/3pl/KLu94ewIIIAAAgUloNFcCprq7zwFAQQQCJuA5tZMpShN7c6dOz1NJ06caE2aNPHU5XVl/fr1Ke1au3btlNrFa1S2bNl4m/JUv3bt2pj9guZWjWkUp0LfJbqDyP7RrO7dNOrUHbj98ccf7euvv7bmzZu7mznLCppu2rQpWq8px84888zounshLPfa3SeWEUAAAQQKRoDAacE45uxR/MFJpR3xPxwpne/YsWM917hx48bABwxPoxRXlLIkqPiDukcffbTpgSWd4j9GvDTE6RyTtggggAACCCCQXQF9qc4X69m9B5wdAQQQQAABBBBAIFYgaH7P2FZ5r9m6dWtKO++zzz4ptctUo6CAb36Cu0pF7A6cJnL517/+ZVdeeaW5Uwi/8sorgd9rvvjiix6S4447zuIN+AjLvfZ0mBUEEEAAgQIRYI7TAmHM3YP4A4tBaXqVrz9ortCCuuqgN7x0bH/f2rRpk9Yp9QCjt8jcJej63NtZRgABBBBAAAEEEEAAAQQQQAABBBBAIC8Cf/75Z152S3kfpYdNpRQrViyVZhlr4043rJNqYES8qbtS6ZQ/NW+i1MQKfPrTwCtw6i8bNmyw9957z1N93nnnedbdK2G51+4+sYwAAgggUDAC6Q3fK5hzcpSQCOhhS6kp3CUosBj0MOHeJ7/LzZo1izmE3hrzPwSlO1o0aI7UoOuLOTkVCCCAAAIIIIAAAggggAACCCCAAAIIpCkQFAzs3r27adRjQZREAcKCOH5hHcM/ulSjP/W9X9WqVdM+pVIhr1q1yrNfsjlZla7XPXfpwoULbd68eaY5UCPljTfe8EwFor4df/zxkc0xP7nXMSRUIIAAAkVGgMBpkbmV6V/InDlzbPv27Z4dg4KT3377radN+fLlbdSoUZ66/KwEBTP9KYT1ptzhhx+e1mn8I1ZLly5tTZs2TesYNEYAAQQQQAABBBBAAAEEEEAAAQQQQCAVgcqVK8c00/dRPXv2jKnfnSoaNGgQc7nKbpeXwOnq1as9aXd14GSB0xNPPNGU3tedMlgDRdyB0xdeeMHTxzPOOMNKlCjhqXOvcK/dGiwjgAACRUuAwGnRup9pXY0/sKi5TTXHqb/88MMPniqluCjsBz5/3xo1apR2Cg9/8LVly5Zpz5HquXBWEEAAAQQQQAABBBBAAAEEEEAAAQQQiCOguUUVbNuxY0e0xffffx9d3l0X6tevH3PpCpwedthhMfXJKjRa1F+SBU5LlSplvXr1ssceeyy6qwKnN910k7Ou6b4mTZoU3aaFRGl6tZ177eFiBQEEEChSAsxxWqRuZ3oX4w9OKrC45557eg6ya9cuW7ZsmaeuYsWKnvXCWPH3LWhUarLzFsQxkp2D7QgggAACCCCAAAIIIIAAAggggAACCEhAQdNWrVp5MPIaOFXwdebMmZ5PYc+r6el4Aa4oxXDJkiU9RwyaYsvTIM7Kyy+/HLMlWeBUOyhdr7vMnz/fvvvuO6fqtdde8wS7W7RoYYceeqi7ecwy9zqGhAoEEECgyAgQOC0ytzL9C/GPyAxK06sUuf4J5ZXaIi9l7dq1noe9r776KvAwmudg9uzZnm3pBk5//vln0/ncJej63NtZRgABBBBAAAEEEEAAAQQQQAABBBBAID8Cbdq08ez+xx9/2IQJEzx1qayMGDHCmbZKU1fpo+Nqfs9cLHvssUdMOt2RI0fali1b0rocfWfoD5w2bNjQatWqlfQ4Rx11lPlTBmvUqYo/TW+y0aaRk3GvIxL8RAABBIqWAIHTonU/U74a5fRftGiRp3284KT/jTAFJdMtehDq2LGj54HvtttuCzyMJmffvHmzZ1u8vnkauVb8o021Kd1juA7HIgIIIIAAAggggAACCCCAAAIIIIAAAkkFgqa3Gjp0aNL93A02bNhg/u/NNE9n+fLl3c1yalmpct1F6XGfffZZd1XS5YkTJ9qaNWs87a6//npTYDaV4h91qsDpqlWrbMqUKdHd9T1onz59ouuJFrjXiXTYhgACCOSuQGr/V8nd66PncQSU6kNpeN0lXmDR/9aW5jxNN3h6ySWX2DfffBM9XfHixW3IkCHRdfeCP+ipeQiaNWvmbpJ02X8MTTZft27dpPvRAAEEEEAAAQQQQAABBBBAAAEEEEAAgbwK6Pu1tm3benafNm2affDBB566RCu33357TCa1VIN5iY6bzW1XXXWV1ahRw9OFYcOG2YoVKzx18Va2b99ugwcP9myuV69eykFO7XjmmWd6MuvNmTPH7rjjDs9IXgWoNX9pKoV7nYoSbRBAAIHcEyBwmnv3rEB67E/TW716ddN8A0HF/7CnNm+//XZQ08C6p556yvRxl6uvvtqaN2/uroou+4Oeaucf9RptHGfBf33xgsJxdqcaAQQQQAABBBBAAAEEEEAAAQQQQACBPAncdNNNngCdDnLKKafYc889l/B4ysDWr18/U0DRXdq1a2cK6OVyKVu2rN16662eS/jpp5+sdevWpvlGE5Vt27bZhRde6BmUofbXXXedaXBGqqVOnTomS3d56KGH3Kt27rnnetaTrXCvkwmxHQEEEMg9AQKnuXfPCqTH/uCk5kqIV0444YSYTQp8JgueKuXGBRdc4DzYuA9w0kkn2c033+yu8iz7+5Zu0LMg5kj1dIgVBBBAAAEEEEAAAQQQQAABBBBAAAEEUhTo0KGDKYWsu2gaq7POOsv69u1rb775pi1ZssTZrKDgV199ZU8//bS1atXKHnvsMfdupixqzz//vO25556e+lxc0dyhTZs29XR92bJldswxxzjB4smTJ5vmhI2UP//805555hk78sgjY9L6HnjggeZPvRvZL9FP/z7ujHwaWNK1a9dEu8ds417HkFCBAAII5LxA6q/k5PylcgFugXSCkyeffLIzP6h7Hz3U9ejRw0m3q4nQW7RoYRUqVDAFS+fOnWtqe/fdd8ekFTn22GPtpZdeivs2mB6I/G+ZpRs41Ryp/snl9YBFQQABBBBAAAEEEEAAAQQQQAABBBBAIBMCGom4cOFCe/nllz2nGzlypOmjou/SNm3aZBoEEFQ0d6fmAd1vv/2CNudcna5n1KhRpu8Hf//992j/tRxJw1usWDGrXbu26TtCfc8YVKpVq2bvvPNO3O8Xg/aJ1GleUk0pptG9/qLAdjojWCP7c68jEvxEAAEEioYAI06Lxn1M6yqWLl1qq1ev9uyTLDj58MMPW5kyZTz77Nixw2644QbTm1WVKlVy8v9XqVLFWb/mmmtigqZKhfH666+b5iyNV2bNmuWZV0Dt0g16ugO8kfMkGlEbacNPBBBAAAEEEEAAAQQQQAABBBBAAAEECkJAI0RffPFFu/LKK+MeTqMr4wVNFSydNGmSdenSJe7+ubhB39FNmTLFNLozqGgEqEbjxguaaqqxiRMnWuPGjYN2T1pXrlw5O/XUUwPbpZumN3IQ7nVEgp8IIIBA0RAgcFo07mNaV+Gf/1NvciULLGr7a6+9FneuUT3UrFu3LrAfe+21lw0dOtR5qPEHX/07+IOeCsjWr1/f3yzhuv8Y2r9y5coJ92EjAggggAACCCCAAAIIIIAAAggggAACBSmg79yUke3jjz920vCmcmwF9jSf55w5c5zBCansk2ttmjVrZgsWLLBBgwZZiRIlUup+6dKlncx32u/QQw9NaZ94jfzpetVOg0qaNGkSb5ek9dzrpEQ0QAABBHJGgFS9OXOrCq6j/sBigwYNnBGjyc6gN9xmzpzpzMXgD74G7bv33nub0vwqXUWqwU9/3xSw1YNHOsXft3RHrKZzLtoigAACCCCAAAIIIIAAAggggAACCBQtgXHjxpk+BVXatm1rM2bMMH1n9dZbb9nnn39uq1atctLV7rvvvrb//vs7n6OPPtq6d+8ek/UtWT8+++yzZE3ibr/88stNn7wUjfp0zxGazjEqVqxow4cPd1L0vvvuuzZ+/Hj76aefnCx5miJMc7vq06hRIzv++OOtU6dOVrZs2XROEbetUgXntd9xD/r/Gwr7Xic7P9sRQAABBPIvQOA0/4Y5dwS96aZPXoomcJ8+fbpNmzbNXnnlFZs9e7bzoKc5RZVCpGbNms6DntLy6qFGb4OlU/zzPqSzb6St5lilIIAAAggggAACCCCAAAIIIIAAAgggEBYBDQxo3bq18wlLn8LQDwWOzz//fOcThv4URB+41wWhyDEQQACB7AkQOM2efc6eWf/zb9OmjfPJ2Yug4wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4BJjj1IXBIgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII7J4CjDg1s3nz5tmmTZsy8hug0ZplypTJ6Pk0uTnlfwKZvNeal+HAAw/MCL1+r7jPGaHmJAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFBEBXb7wKkmAtek3evXr8/ILW7YsKGtWbMmY+fTBOrfffddRq4t7CfJ9L3eY489bOfOnRlh4T5nhJmTIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQBEW2O1T9X7//fcZC2Lq96hJkyYZPZ8mnaf8TyDT9zpTQVNdHfeZ33IEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIH8Cu/2I06pVq9qUKVPyp5jG3hUrVrRBgwalsUf+mtapUyd/ByhCe2fyXm/YsME2b95s1atXz4gg9zkjzJwEAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEirDAbh84rVy5srVr164I32IuLSLAvY5I8BMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMAvsNun6vWDsI4AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAArufAIHT3e+ec8UIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOATyPlUvVOnTrUBAwb4LotVBBBAAIFcEtDfcgoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAtkUyPnA6bx580wfCgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJBXgZxL1VuqVKm8Xiv7IYAAAgjkiAB/63PkRtFNBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgCAnkXOC0W7duRYifS0EAAQQQCBLgb32QCnUIIIAAAggggAACCCCAAAIIIIAAAggggAAChSmQc6l6O3fubB988IGNHz/etm3bVpg2HBsBBBBAIMMCGmmqoKn+1lMQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMikQM4FToWjL9T5Uj2TvyacCwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGiLZBzqXqL9u3g6hBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIBsCBE6zoc45EUAAAQQQQAABBBBAAAEEEEAAAQQQQAAHghKWAAAZ1ElEQVQBBBBAAAEEEEAgVAIETkN1O+gMAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghkQ4DAaTbUOScCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCIRKgMBpqG4HnUEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgWwIEDjNhjrnRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBUAkQOA3V7aAzCCCAAAIIIIAAAggggAACCCCAAAIIIIBA4QicdtppVq9evejn3//+d+GcKEtH7dGjhxUvXjz6ufbaawu1J5k+X6FeDAdHAAEEEHAEiuOAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUPQFli9fbj/99FP0QletWhVdLgoLO3futL///jt6KVovzJLp8xXmtXBsBBBAAIH/CTDilN8EBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDY7QUInO72vwIAIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAgVN+BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYLcXYI7T3f5XAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQ2B0EXn/9ddu+fXv0UkuXLh1dZgEBBBBAAAEEzAic8luAAAIIIIAAAggggAACCCCAAAIIIIAAAgjsBgL77rvvbnCVXCICCCCAAAJ5FyBVb97t2BMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBIqIAIHTInIjuQwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMi7AKl6827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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 2 An array of sequences\n",
    "## Overview of built-in sequences\n",
    "The standard library offers a rich selection of sequence types implemented in C:\n",
    "### Container sequences\n",
    "list, tuple and collections.deque can hold items of different types.\n",
    "### Flat sequences\n",
    "str, bytes, bytearray, memoryview and array.array hold items of one type.\n",
    "Container sequences hold references to the objects they contain, which may be of any\n",
    "type, while flat sequences physically store the value of each item within its own memory\n",
    "space, and not as distinct objects. Thus, flat sequences are more compact, but they are\n",
    "limited to holding primitive values like characters, bytes and numbers\n",
    "Another way of grouping sequence types is by mutability:\n",
    "### Mutable sequences\n",
    "list, bytearray, array.array, collections.deque and memoryview\n",
    "### Immutable sequences\n",
    "tuple, str and bytes\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "## List comprehensions and generator expressions\n",
    "A quick way to build a sequence is using a list comprehension (if the target is a list)\n",
    "or a generator expression (for all other kinds of sequences)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[36, 162, 163, 165, 8364, 164, 34081, 24378, 97]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols = '$¢£¥€¤蔡强a'\n",
    "codes = []\n",
    "for symbol in symbols:\n",
    "    codes.append(ord(symbol))\n",
    "codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[36, 162, 163, 165, 8364, 164, 34081, 24378, 97]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codes = [ord(symbol) for symbol in symbols]\n",
    "codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ord(c)\n",
    "Given a string representing one Unicode character, return an integer representing the Unicode code point of that character. For example, ord('a') returns the integer 97 and ord('€') (Euro sign) returns 8364. This is the inverse of chr()."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python code, line breaks are ignored inside pairs of [], {} or ().\n",
    "So you can build multi-line lists, listcomps, genexps, dictionaries etc.\n",
    "without using the ugly \\ line continuation escape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Listcomps versus map and filter\n",
    "### Cartesian products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('black', 'S'),\n",
       " ('black', 'M'),\n",
       " ('black', 'L'),\n",
       " ('white', 'S'),\n",
       " ('white', 'M'),\n",
       " ('white', 'L')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors = ['black', 'white']\n",
    "sizes = ['S', 'M', 'L']\n",
    "tshirts = [(color, size) for color in colors for size in sizes]\n",
    "tshirts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('black', 'S'),\n",
       " ('white', 'S'),\n",
       " ('black', 'M'),\n",
       " ('white', 'M'),\n",
       " ('black', 'L'),\n",
       " ('white', 'L')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tshirts = [(color, size) for size in sizes for color in colors ]\n",
    "tshirts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generator expressions\n",
    "To initialize tuples, arrays and other types of sequences, you could also start from a\n",
    "listcomp but a genexp saves memory because it yields items one by one using the iterator\n",
    "protocol instead of building a whole list just to feed another constructor.\n",
    "Genexps use the same syntax as listcomps, but are enclosed in parenthesis rather than\n",
    "brackets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 162, 163, 165, 8364, 164)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols = '$¢£¥€¤'\n",
    "tuple(ord(symbol) for symbol in symbols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('I', [36, 162, 163, 165, 8364, 164])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import array\n",
    "array.array('I', (ord(symbol) for symbol in symbols))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### class array.array(typecode[, initializer])\n",
    "A new array whose items are restricted by typecode, and initialized from the optional initializer value, which must be a list, a bytes-like object, or iterable over elements of the appropriate type.\n",
    "\n",
    "If given a list or string, the initializer is passed to the new array’s fromlist(), frombytes(), or fromunicode() method (see below) to add initial items to the array. Otherwise, the iterable initializer is passed to the extend() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "black S\n",
      "black M\n",
      "black L\n",
      "white S\n",
      "white M\n",
      "white L\n"
     ]
    }
   ],
   "source": [
    "colors = ['black', 'white']\n",
    "sizes = ['S', 'M', 'L']\n",
    "for tshirt in ('%s %s' % (c, s) for c in colors for s in sizes):\n",
    "    print(tshirt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuples are not just immutable lists\n",
    "Tuples do double-duty: they can be used as immutable lists and also\n",
    "as records with no field names. This use is sometimes overlooked, so we will start with\n",
    "that.\n",
    "### Tuples as records\n",
    "Tuples hold records: each item in the tuple holds the data for one field and the position\n",
    "of the item gives its meaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRA/CE342567\n",
      "ESP/XDA205856\n",
      "USA/31195855\n"
     ]
    }
   ],
   "source": [
    "lax_coordinates = (33.9425, -118.408056)\n",
    "city, year, pop, chg, area = ('Tokyo', 2003, 32450, 0.66, 8014)\n",
    "traveler_ids = [('USA', '31195855'), ('BRA', 'CE342567'),\n",
    "  ('ESP', 'XDA205856')]\n",
    "for passport in sorted(traveler_ids):\n",
    "    print('%s/%s' % passport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USA\n",
      "BRA\n",
      "ESP\n"
     ]
    }
   ],
   "source": [
    "for country, _ in traveler_ids:\n",
    "    print(country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuple unpacking "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.9425"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lax_coordinates = (33.9425, -118.408056)\n",
    "latitude, longitude = lax_coordinates\n",
    "latitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-118.408056"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divmod(20, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=(20,8)\n",
    "divmod(*t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quotient, remainder = divmod(*t)\n",
    "quotient, remainder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'idrsa.pub'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "_, filename = os.path.split('/home/luciano/.ssh/idrsa.pub')\n",
    "filename"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes when we only care about certain parts of a tuple when unpacking, a dummy\n",
    "variable like _ is used as placeholder, as in the example above.\n",
    "28 | Chapter 2: An array of sequences\n",
    "\n",
    "Another way of focusing on just some of the items when unpacking a tuple is to use the *, as we’ll see right away.\n",
    "### Using * to grab excess items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, [2, 3, 4])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b, *rest = range(5)\n",
    "a,b,rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, [2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b, *rest = range(3)\n",
    "a,b,rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, [])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b, *rest = range(2)\n",
    "a,b,rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, [1, 2], 3, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, *body, c, d = range(5)\n",
    "a, body, c, d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1], 2, 3, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "*head, b, c, d = range(5)\n",
    "head, b, c, d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, a powerful feature of tuple unpacking is that it works with nested structures.\n",
    "### Nested tuple unpacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                |   lat.    |   long.  \n",
      "Mexico City     |   19.4333 |  -99.1333\n",
      "New York-Newark |   40.8086 |  -74.0204\n",
      "Sao Paulo       |  -23.5478 |  -46.6358\n"
     ]
    }
   ],
   "source": [
    "metro_areas = [\n",
    "('Tokyo', 'JP', 36.933, (35.689722, 139.691667)), #\n",
    "('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889)),\n",
    "('Mexico City', 'MX', 20.142, (19.433333, -99.133333)),\n",
    "('New York-Newark', 'US', 20.104, (40.808611, -74.020386)),\n",
    "('Sao Paulo', 'BR', 19.649, (-23.547778, -46.635833))]\n",
    "print('{:15} | {:^9} | {:^9}'.format('', 'lat.', 'long.'))\n",
    "fmt = '{:15} | {:9.4f} | {:9.4f}'\n",
    "for name, cc, pop, (latitude, longitude) in metro_areas: #\n",
    "    if longitude <= 0: #\n",
    "        print(fmt.format(name, latitude, longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### str.format(*args, **kwargs)\n",
    "Perform a string formatting operation. The string on which this method is called can contain literal text or replacement fields delimited by braces {}. Each replacement field contains either the numeric index of a positional argument, or the name of a keyword argument. Returns a copy of the string where each replacement field is replaced with the string value of the corresponding argument.\n",
    " \"The sum of 1 + 2 is {0}\".format(1+2)\n",
    " 'The sum of 1 + 2 is 3'\n",
    " See Format String Syntax for a description of the various formatting options that can be specified in format strings.\n",
    "\n",
    "Note When formatting a number (int, float, complex, decimal.Decimal and subclasses) with the n type (ex: '{:n}'.format(1234)), the function temporarily sets the LC_CTYPE locale to the LC_NUMERIC locale to decode decimal_point and thousands_sep fields of localeconv() if they are non-ASCII or longer than 1 byte, and the LC_NUMERIC locale is different than the LC_CTYPE locale. This temporary change affects other threads.\n",
    "Changed in version 3.6.5: When formatting a number with the n type, the function sets temporarily the LC_CTYPE locale to the LC_NUMERIC locale in some cases.\n",
    "### Named tuples\n",
    "The collections.namedtuple function is a factory that produces subclasses\n",
    "enhanced with field names and a class name — which helps debugging.\n",
    "\n",
    "Instances of a class that you build with namedtuple take exactly the\n",
    "same amount of memory as tuples because the field names are stored\n",
    "in the class. They use less memory than a regular object because\n",
    "they do store attributes in a per-instance __dict__.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tokyo'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "City = namedtuple('City', 'name country population coordinates')\n",
    "tokyo = City('Tokyo', 'JP', 36.933, (35.689722, 139.691667))\n",
    "tokyo.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35.689722, 139.691667)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokyo.coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A named tuple type has a few attributes in addition to those inherited from tuple.\n",
    "Example 2-10 shows the most useful: the _fields class attribute, the class method\n",
    "_make(iterable) and the _asdict() instance method.\n",
    "_fields is a tuple with the field names of the class.\n",
    "_make() lets you instantiate a named tuple from an iterable; City(*delhi_da\n",
    "ta) would do the same.\n",
    "_asdict() returns a collections.OrderedDict built from the named tuple\n",
    "instance. That can be used to produce a nice display of city data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('name', 'country', 'population', 'coordinates')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "City._fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('name', 'Delhi NCR'),\n",
       "             ('country', 'IN'),\n",
       "             ('population', 21.935),\n",
       "             ('coordinates', LatLong(lat=28.613889, long=77.208889))])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LatLong = namedtuple('LatLong', 'lat long')\n",
    "delhi_data = ('Delhi NCR', 'IN', 21.935, LatLong(28.613889, 77.208889))\n",
    "delhi = City._make(delhi_data)\n",
    "delhi._asdict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuples as immutable lists\n",
    "## Slicing\n",
    "### Why slices and range exclude the last item\n",
    "The Pythonic convention of excluding the last item in slices and ranges works well with\n",
    "the zero-based indexing used in Python, C and many other languages. Some convenient\n",
    "features of the convention are:\n",
    "• It’s easy to see the length of a slice or range when only the stop position is given:\n",
    "range(3) and my_list[:3] both produce three items.\n",
    "• It’s easy to compute the length of a slice or range when start and stop are given: just\n",
    "subtract stop - start.\n",
    "• It’s easy to split a sequence in two parts at any index x, without overlapping: simply\n",
    "get my_list[:x] and my_list[x:]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 20]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [10, 20, 30, 40, 50, 60]\n",
    "l[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[30, 40, 50, 60]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 20, 30]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[40, 50, 60]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slice objects\n",
    "This is no secret, but worth repeating just in case: s[a:b:c] can be used to specify a\n",
    "stride or step c, causing the resulting slice to skip items. The stride can also be negative,\n",
    "returning items in reverse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bye'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s='bicycle'\n",
    "s[::3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elcycib'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'eyb'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1909  Pimoroni PiBrella                    $17.50   3   $52.50\n",
      "1489  6mm Tactile Switch x20               $4.95    2   $9.90\n",
      "1510  Panavise Jr. - PV-201                $28.00   1   $28.00\n",
      "1601  PiTFT Mini Kit 320x240               $34.95   1   $34.95\n",
      "\n",
      "   $17.50    Pimoroni PiBrella                   $52.50\n",
      "   $4.95     6mm Tactile Switch x20              $9.90\n",
      "   $28.00    Panavise Jr. - PV-201               $28.00\n",
      "   $34.95    PiTFT Mini Kit 320x240              $34.95\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "invoice = \"\"\"\n",
    "... 0.....6.................................40........52...55........\n",
    "... 1909  Pimoroni PiBrella                    $17.50   3   $52.50\n",
    "... 1489  6mm Tactile Switch x20               $4.95    2   $9.90\n",
    "... 1510  Panavise Jr. - PV-201                $28.00   1   $28.00\n",
    "... 1601  PiTFT Mini Kit 320x240               $34.95   1   $34.95\n",
    "... \"\"\"\n",
    "SKU = slice(0, 6)\n",
    "DESCRIPTION = slice(6, 40)\n",
    "UNIT_PRICE = slice(40, 52)\n",
    "QUANTITY = slice(52, 55)\n",
    "ITEM_TOTAL = slice(55, None)\n",
    "line_items = invoice.split('\\n')[2:]\n",
    "for item in line_items :\n",
    "    print(item)\n",
    "for item in line_items:\n",
    "    print(item[UNIT_PRICE], item[DESCRIPTION],item[ITEM_TOTAL])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-dimensional slicing and ellipsis\n",
    "The built-in sequence types in Python are one-dimensional, so they support only one\n",
    "index or slice, and not a tuple of them.\n",
    "The ellipsis — written with three full stops ... and not … (Unicode U+2026) — is recognized\n",
    "as a token by the Python parser. It is an alias to the Ellipsis object, the single\n",
    "instance of the ellipsis class4.\n",
    "### Assigning to slices\n",
    "Mutable sequences can be grafted, excised and otherwise modified in-place using slice\n",
    "notation on the left side of an assignment statement or as the target of a del statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=list(range(10))\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 20, 30, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[2:5] = [20, 30]\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 20, 30, 5, 8, 9]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del l[5:7]\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 20, 11, 5, 22, 9]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[3::2] = [11, 22]\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only assign an iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-e500d102d83b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: can only assign an iterable"
     ]
    }
   ],
   "source": [
    "l[2:5] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using + and * with sequences\n",
    "Python programmers expect that sequences support + and *. Usually both operands of + must be of the same sequence type, and neither of them is modified but a new sequence\n",
    "of the same type is created as result of the concatenation.\n",
    "To concatenate multiple copies of the same sequence, multiply it by an integer. Again,\n",
    "a new sequence is created:\n",
    "Both + and * always create a new object, and never change their operands.\n",
    "Beware of expressions like a * n when a is a sequence containing\n",
    "mutable items because the result may surprise you. For example,\n",
    "trying to initialize a list of lists as my_list = [[]] * 3 will result in\n",
    "a list with three references to the same inner list, which is probably\n",
    "not what you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1, 2, 3]\n",
    "l*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdabcdabcdabcdabcd'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 * 'abcd'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building lists of lists\n",
    "Sometimes we need to initialize a list with a certain number of nested lists, for example,\n",
    "to distribute students in a list of teams or to represent squares on a game board. The\n",
    "best way of doing so is with a list comprehension, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['_', '_', '_'], ['_', '_', '_'], ['_', '_', '_']]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "board = [['_'] * 3 for i in range(3)]\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['_', '_', '_'], ['_', '_', 'x'], ['_', '_', '_']]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "board[1][2]='x'\n",
    "board"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list with with three references to the same list is useless."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['_', '_', '_'], ['_', '_', '_'], ['_', '_', '_']]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weird_board = [['_'] * 3] * 3\n",
    "weird_board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['_', '_', 'x'], ['_', '_', 'x'], ['_', '_', 'x']]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weird_board[1][2]='x'\n",
    "weird_board"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augmented assignment with sequences\n",
    "The augmented assignment operators += and *= behave very differently depending on\n",
    "the first operand.\n",
    "The special method that makes += work is __iadd__ (for “in-place addition”). However,\n",
    "if __iadd__ is not implemented, Python falls back to calling __add__.\n",
    "In general, for mutable sequences it is a good bet that __iadd__ is implemented and\n",
    "that += happens in-place. For immutable sequences, clearly there is no way for that to\n",
    "happen.\n",
    "What I just wrote about += also applies to *=, which is implemented via __imul__. The\n",
    "__iadd__ and __imul__ special methods are discussed in Chapter 13."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111041608"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[1,2,3]\n",
    "id(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l*=2\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111041608"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110571664"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=(1,2,3)\n",
    "id(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t*=2\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110432616"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A += assignment puzzler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-3b2a7ae03f89>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m40\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "t = (1, 2, [30, 40])\n",
    "t[2]+=[50,60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, [30, 40, 50, 60])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I take three lessons from this:\n",
    "• Putting mutable items in tuples is not a good idea.\n",
    "• Augmented assignment is not an atomic operation — we just saw it throwing an\n",
    "exception after doing part of its job.\n",
    "• Inspecting Python bytecode is not too difficult, and is often helpful to see what is\n",
    "going on under the hood.\n",
    "## list.sort and the sorted built-in function\n",
    "The list.sort method sorts a list in-place, that is, without making a copy. It returns\n",
    "None to remind us that it changes the target object, and does not create a new list. This\n",
    "is an important Python API convention: functions or methods that change an object\n",
    "in-place should return None to make it clear to the caller that the object itself was\n",
    "changed, and no new object was created. The same behavior can be seen, for example,\n",
    "in the random.shuffle function.\n",
    "In contrast, the built-in function sorted creates a new list and returns it. In fact, sor\n",
    "ted accepts any iterable object as argument, including immutable sequences and generators\n",
    "(see Chapter 14). Regardless of the type of iterable given to sorted, it always\n",
    "returns a newly created list.\n",
    "Both list.sort and sorted take two optional, keyword-only arguments: key and re\n",
    "verse.\n",
    "reverse\n",
    "If True, the items are returned in descending order, i.e. by reversing the comparison\n",
    "of the items. The default is False.\n",
    "key\n",
    "A one-argument function that will be applied to each item to produce its sorting\n",
    "key. For example, when sorting a list of strings, key=str.lower can be used to\n",
    "perform a case-insensitive sort, and key=len will sort the strings by character\n",
    "length. The default is the identity function, i.e. the items themselves are compared.\n",
    "\n",
    "the sorting algorithm used in Python — is stable, i.e. it preserves the relative ordering of items that compare equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'banana', 'grape', 'raspberry']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits = ['grape', 'raspberry', 'apple', 'banana']\n",
    "sorted(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['grape', 'raspberry', 'apple', 'banana']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['raspberry', 'grape', 'banana', 'apple']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(fruits,reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['grape', 'apple', 'banana', 'raspberry']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(fruits,key=len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['raspberry', 'banana', 'grape', 'apple']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(fruits,key=len,reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['grape', 'raspberry', 'apple', 'banana']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fruits.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'banana', 'grape', 'raspberry']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Managing ordered sequences with bisect\n",
    "The bisect module offers two main functions — bisect and insort — that use the\n",
    "binary search algorithm to quickly find and insert items in any sorted sequence.\n",
    "Searching with bisect\n",
    "bisect(haystack, needle) does a binary search for needle in haystack — which\n",
    "must be a sorted sequence — to locate the position where needle can be inserted while\n",
    "maintaining haystack in ascending order. In other words, all items appearing up to that\n",
    "position are less or equal to needle. You could use the result of bisect(haystack,\n",
    "needle) as the index argument to haystack.insert(index, needle), but using in\n",
    "sort does both steps, and is faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEMO: bisect\n",
      "haystack ->  1  4  5  6  8 12 15 20 21 23 23 26 29 30\n",
      "31 @ 14  | | | | | | | | | | | | | |31\n",
      "30 @ 14  | | | | | | | | | | | | | |30\n",
      "29 @ 13  | | | | | | | | | | | | |29\n",
      "23 @ 11  | | | | | | | | | | |23\n",
      "22 @  9  | | | | | | | | |22\n",
      "10 @  5  | | | | |10\n",
      " 8 @  5  | | | | |8 \n",
      " 5 @  3  | | |5 \n",
      " 2 @  1  |2 \n",
      " 1 @  1  |1 \n",
      " 0 @  0 0 \n"
     ]
    }
   ],
   "source": [
    "import bisect\n",
    "import sys\n",
    "HAYSTACK = [1, 4, 5, 6, 8, 12, 15, 20, 21, 23, 23, 26, 29, 30]\n",
    "NEEDLES = [0, 1, 2, 5, 8, 10, 22, 23, 29, 30, 31]\n",
    "ROW_FMT = '{0:2d} @ {1:2d} {2}{0:<2d}'\n",
    "def demo(bisect_fn):\n",
    "    for needle in reversed(NEEDLES):\n",
    "        position = bisect_fn(HAYSTACK, needle)\n",
    "        offset = position * ' |'\n",
    "        print(ROW_FMT.format(needle, position, offset))\n",
    "if __name__ == '__main__':\n",
    "    if sys.argv[-1] == 'left':\n",
    "        bisect_fn = bisect.bisect_left\n",
    "    else:\n",
    "        bisect_fn = bisect.bisect\n",
    "print('DEMO:', bisect_fn.__name__)\n",
    "print('haystack ->', ' '.join('%2d' % n for n in HAYSTACK))\n",
    "demo(bisect_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The behavior of bisect can be fine-tuned in two ways.\n",
    "First, a pair of optional arguments lo and hi allow narrowing the region in the sequence\n",
    "to be searched when inserting. lo defaults to 0 and hi to the len() of the sequence.\n",
    "Second, bisect is actually an alias for bisect_right, and there is a sister function called\n",
    "bisect_left. Their difference is apparent only when the needle compares equal to an\n",
    "item in the list: bisect_right returns an insertion point after the existing item, and\n",
    "bisect_left returns the position of the existing item, so insertion would occur before\n",
    "it. With simple types like int this makes no difference, but if the sequence contains\n",
    "objects that are distinct yet compare equal, then it may be relevant. For example, 1 and\n",
    "1.0 are distinct, but 1 == 1.0 is True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F', 'A', 'C', 'C', 'B', 'A', 'A']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):\n",
    "    i = bisect.bisect(breakpoints, score)\n",
    "    return grades[i]\n",
    "[grade(score) for score in [33, 99, 77, 70, 89, 90, 100]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Inserting with bisect.insort\n",
    "Sorting is expensive, so once you have a sorted sequence, it’s good to keep it that way.\n",
    "That is why bisect.insort was created.\n",
    "insort(seq, item) inserts item into seq so as to keep seq in ascending order.\n",
    "\n",
    "Like bisect, insort takes optional lo, hi arguments to limit the search to a subsequence.\n",
    "There is also an insort_left variation that uses bisect_left to find insertion\n",
    "points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 8--> [8]\n",
      " 7--> [7, 8]\n",
      " 0--> [0, 7, 8]\n",
      " 9--> [0, 7, 8, 9]\n",
      " 7--> [0, 7, 7, 8, 9]\n",
      " 5--> [0, 5, 7, 7, 8, 9]\n",
      " 6--> [0, 5, 6, 7, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "import bisect\n",
    "import random\n",
    "SIZE=7\n",
    "#random.seed(1729)\n",
    "mylist=[]\n",
    "for i in range(SIZE) :\n",
    "    newitem=random.randrange(SIZE*2)\n",
    "    bisect.insort(mylist,newitem)\n",
    "    print('%2d-->' %newitem, mylist)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When a list is not the answer\n",
    "The list type is flexible and easy to use, but depending on specific requirements there\n",
    "are better options. For example, if you need to store 10 million of floating point values\n",
    "an array is much more efficient, because an array does not actually hold full-fledged\n",
    "float objects, but only the packed bytes representing their machine values — just like\n",
    "an array in the C language. On the other hand, if you are constantly adding and removing\n",
    "items from the ends of a list as a FIFO or LIFO data structure, a deque (double-ended\n",
    "queue) works faster.\n",
    "### Arrays\n",
    "#### array — Efficient arrays of numeric values\n",
    "This module defines an object type which can compactly represent an array of basic values: characters, integers, floating point numbers. Arrays are sequence types and behave very much like lists, except that the type of objects stored in them is constrained. The type is specified at object creation time by using a type code, which is a single character. The following type codes are defined:\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6042542967601301"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from array import array\n",
    "from random import random\n",
    "floats=array('d',(random() for i in range(10**7)))\n",
    "floats[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fp = open('floats.bin', 'wb')\n",
    "floats.tofile(fp)\n",
    "fp.close()\n",
    "floats2=array('d')\n",
    "fp=open('floats.bin','rb')\n",
    "floats2.fromfile(fp,10**7)\n",
    "floats2[-1]\n",
    "fp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floats==floats2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Memory views\n",
    "A memoryview is essentially a generalized NumPy array structure in Python itself\n",
    "(without the math). It allows you to share memory between data-structures (things like\n",
    "PIL images, SQLlite databases, NumPy arrays, etc.) without first copying. This is very\n",
    "important for large data sets.\n",
    "Using notation similar to the array module, the memoryview.cast method lets you\n",
    "change the way multiple bytes are read or written as units without moving bits\n",
    "around — just like the C cast operator. memoryview.cast returns yet another memory\n",
    "view object, always sharing the same memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from array import array\n",
    "numbers = array('h', [-2, -1, 0, 1, 2])\n",
    "memv = memoryview(numbers)\n",
    "len(memv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memv[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[254, 255, 255, 255, 0, 0, 1, 0, 2, 0]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memv_oct = memv.cast('B')\n",
    "memv_oct.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('h', [-2, -1, 1024, 1, 2])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memv_oct[5] = 4\n",
    "numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meanwhile, if you are doing advanced numeric processing in arrays, then you should\n",
    "be using the NumPy and SciPy libraries. We’ll take a brief look at them right away.\n",
    "### NumPy and SciPy\n",
    "SciPy is a library, written on top of NumPy, offering many scientific computing algorithms\n",
    "from linear algebra, numerical calculus and statistics. SciPy is fast and reliable\n",
    "because it leverages the widely-used C and Fortran codebase from the Netlib Repository.\n",
    "In other words, SciPy gives scientists the best of both worlds: an interactive prompt\n",
    "and high-level Python APIs, together with industrial-strength number crunching functions\n",
    "optimized in C and Fortran."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a=np.arange(12)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape=3,4\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8,  9, 10, 11])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 9])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4,  8],\n",
       "       [ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was just an appetizer. NumPy and SciPy are formidable libraries, and are the foundation\n",
    "of other awesome tools such as the Pandas and Blaze data analysis libraries, which\n",
    "provide efficient array types that can to hold non-numeric data as well as import/export\n",
    "functions compatible with many different formats like .csv, .xls, SQL dumps, HDF5\n",
    "etc. These packages deserve entire books about them.\n",
    "### Deques and other queues\n",
    "The class collections.deque is a thread-safe double-ended queue designed for fast\n",
    "inserting and removing from both ends. It is also the way to go if you need to keep a list\n",
    "of “last seen items” or something like that, because a deque can be bounded — i.e. created\n",
    "with a maximum length and then, when it is full, it discards items from the opposite\n",
    "end when you append new ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deque([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import deque\n",
    "dq = deque(range(10), maxlen=10)\n",
    "dq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deque([7, 8, 9, 0, 1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dq.rotate(3)\n",
    "dq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deque([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dq.rotate(-4)\n",
    "dq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deque([-1, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dq.appendleft(-1)\n",
    "dq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deque([3, 4, 5, 6, 7, 8, 9, 11, 22, 33])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dq.extend([11, 22, 33])\n",
    "dq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deque([40, 30, 20, 10, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dq.extendleft([10, 20, 30, 40])\n",
    "dq"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note that extendleft(iter) works by appending each successive item of the\n",
    "iter argument to the left of the deque, therefore the final position of the items\n",
    "is reversed.\n",
    "The append and popleft operations are atomic, so deque is safe to use as a LIFO-queue\n",
    "in multi-threaded applications without the need for using locks.\n",
    "\n",
    "queue\n",
    "Provides the synchronized (i.e. thread-safe) classes Queue, LifoQueue and Priori\n",
    "tyQueue. These are used for safe communication between threads. All three classes\n",
    "can be bounded by providing a maxsize argument greater than 0 to the constructor.\n",
    "However, they don’t discard items to make room as deque does. Instead, when the\n",
    "queue is full the insertion of a new item blocks — i.e. it waits until some other thread\n",
    "makes room by taking an item from the queue, which is useful to throttle the number\n",
    "of live threads.\n",
    "multiprocessing\n",
    "Implements its own bounded Queue, very similar to queue.Queue but designed for\n",
    "inter-process communication. There is also has a specialized multiprocess\n",
    "ing.JoinableQueue for easier task management.\n",
    "asyncio\n",
    "Newly added to Python 3.4, asyncio provides Queue, LifoQueue, PriorityQueue\n",
    "and JoinableQueue with APIs inspired by the classes in queue and multiprocess\n",
    "ing, but adapted for managing tasks in asynchronous programming.\n",
    "heapq\n",
    "In contrast to the previous three modules, heapq does not implement a queue class,\n",
    "but provides functions like heappush and heappop that let you use a mutable sequence\n",
    "as a heap queue or priority queue.\n",
    "\n",
    "# CHAPTER 3 Dictionaries and sets\n",
    "The dict type is not only widely used in our programs but also a fundamental part of\n",
    "the Python implementation. Module namespaces, class and instance attributes and\n",
    "function keyword arguments are some of the fundamental constructs where dictionaries\n",
    "are deployed. The built-in functions live in __builtins__.__dict__.\n",
    "Because of their crucial role, Python dicts are highly optimized. Hash tables are the\n",
    "engines behind Python’s high performance dicts.\n",
    "## Generic mapping types\n",
    "![image.png](attachment:image.png)\n",
    "All mapping types in the standard library use the basic dict in their implementation,\n",
    "so they share the limitation that the keys must be hashable (the values need not be\n",
    "hashable, only the keys)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import abc\n",
    "my_dict = {}\n",
    "isinstance(my_dict, abc.Mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An object is hashable if it has a hash value which never changes during its lifetime (it\n",
    "needs a __hash__() method), and can be compared to other objects (it needs an\n",
    "__eq__() method). Hashable objects which compare equal must have the same hash\n",
    "value. […]\n",
    "The atomic immutable types (str, bytes, numeric types) are all hashable. A frozen\n",
    "set is always hashable, because its elements must be hashable by definition. A tuple is\n",
    "hashable only if all its items are hashable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8027212646858338501"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt = (1, 2, (30, 40))\n",
    "hash(tt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-d2eaad7bd112>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m40\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mhash\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "tl = (1, 2, [30, 40])\n",
    "hash(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4118419923444501110"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf = (1, 2, frozenset([30, 40]))\n",
    "hash(tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User-defined types are hashable by default because their hash value is their id() and\n",
    "they all compare not equal. If an object implements a custom __eq__ that takes into\n",
    "account its internal state, it may be hashable only if all its attributes are immutable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " a = dict(one=1, two=2, three=3)\n",
    " b = {'one': 1, 'two': 2, 'three': 3}\n",
    " c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))\n",
    " d = dict([('two', 2), ('one', 1), ('three', 3)])\n",
    " e = dict({'three': 3, 'one': 1, 'two': 2})\n",
    " a == b == c == d == e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dict comprehensions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bangladesh': 880,\n",
       " 'Brazil': 55,\n",
       " 'China': 86,\n",
       " 'India': 91,\n",
       " 'Indonesia': 62,\n",
       " 'Japan': 81,\n",
       " 'Nigeria': 234,\n",
       " 'Pakistan': 92,\n",
       " 'Russia': 7,\n",
       " 'United States': 1}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIAL_CODES = [\n",
    " (86, 'China'),\n",
    " (91, 'India'),\n",
    " (1, 'United States'),\n",
    " (62, 'Indonesia'),\n",
    " (55, 'Brazil'),\n",
    " (92, 'Pakistan'),\n",
    " (880, 'Bangladesh'),\n",
    " (234, 'Nigeria'),\n",
    " (7, 'Russia'),\n",
    " (81, 'Japan')]\n",
    "\n",
    "country_code= {country: code for (code,country) in DIAL_CODES}\n",
    "country_code\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'UNITED STATES', 7: 'RUSSIA', 55: 'BRAZIL', 62: 'INDONESIA'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{code:country.upper() for country,code in country_code.items() if code<66} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of common mapping methods\n",
    "The basic API for mappings is quite rich. Table 3-1 shows the methods implemented\n",
    "by dict and two of its most useful variations: defaultdict and OrderedDict, both\n",
    "defined in the collections module.\n",
    "### Handling missing keys with setdefault"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ﻿On Tuesday, Amazon briefly became the second publicly traded company after Apple to reach $1 trillion in market cap.\n",
      "\n",
      "1 \n",
      "\n",
      "2 Although the stock closed below the magic number, it's remarkable how quickly it joined the trillion-dollar club. It \n",
      "1 [(0, 93)]\n",
      "after [(0, 71)]\n",
      "Although [(2, 1)]\n",
      "Amazon [(0, 14)]\n",
      "Apple [(0, 77)]\n",
      "became [(0, 29)]\n",
      "below [(2, 27)]\n",
      "briefly [(0, 21)]\n",
      "cap [(0, 114)]\n",
      "closed [(2, 20)]\n",
      "club [(2, 109)]\n",
      "company [(0, 63)]\n",
      "dollar [(2, 102)]\n",
      "how [(2, 67)]\n",
      "in [(0, 104)]\n",
      "it [(2, 51), (2, 79)]\n",
      "It [(2, 115)]\n",
      "joined [(2, 82)]\n",
      "magic [(2, 37)]\n",
      "market [(0, 107)]\n",
      "number [(2, 43)]\n",
      "On [(0, 2)]\n",
      "publicly [(0, 47)]\n",
      "quickly [(2, 71)]\n",
      "reach [(0, 86)]\n",
      "remarkable [(2, 56)]\n",
      "s [(2, 54)]\n",
      "second [(0, 40)]\n",
      "stock [(2, 14)]\n",
      "the [(0, 36), (2, 10), (2, 33), (2, 89)]\n",
      "to [(0, 83)]\n",
      "traded [(0, 56)]\n",
      "trillion [(0, 95), (2, 93)]\n",
      "Tuesday [(0, 5)]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import re\n",
    "WORD_RE = re.compile('\\w+')\n",
    "index = {}\n",
    "with open('zen.txt',encoding='utf-8') as fp:\n",
    "    for line_no, line in enumerate(fp, 0):\n",
    "        print(line_no,line)\n",
    "        for match in WORD_RE.finditer(line):\n",
    "            #print(match)\n",
    "            word = match.group()\n",
    "           # print(word)\n",
    "            column_no = match.start()+1\n",
    "            location = (line_no, column_no)\n",
    "            #print(location)\n",
    "            # this is ugly; coded like this to make a point\n",
    "            #occurrences = index.get(word, [])\n",
    "            #print(occurrences)\n",
    "            #occurrences.append(location)\n",
    "            #print(occurrences)\n",
    "            #index[word] = occurrences\n",
    "            #print(index)            \n",
    "            index.setdefault(word, []).append(location)\n",
    "for word in sorted(index, key=str.upper):\n",
    "    print(word, index[word])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### re.compile(pattern, flags=0)\n",
    "Compile a regular expression pattern into a regular expression object, which can be used for matching using its match(), search() and other methods, described below.\n",
    "\n",
    "The expression’s behaviour can be modified by specifying a flags value. Values can be any of the following variables, combined using bitwise OR (the | operator).\n",
    "#### enumerate(iterable, start=0)\n",
    "Return an enumerate object. iterable must be a sequence, an iterator, or some other object which supports iteration. The __next__() method of the iterator returned by enumerate() returns a tuple containing a count (from start which defaults to 0) and the values obtained from iterating over iterable.\n",
    "seasons = ['Spring', 'Summer', 'Fall', 'Winter']\n",
    "list(enumerate(seasons))\n",
    "[(0, 'Spring'), (1, 'Summer'), (2, 'Fall'), (3, 'Winter')]\n",
    "list(enumerate(seasons, start=1))\n",
    "[(1, 'Spring'), (2, 'Summer'), (3, 'Fall'), (4, 'Winter')]\n",
    "#### re.finditer(pattern, string, flags=0)\n",
    "Return an iterator yielding match objects over all non-overlapping matches for the RE pattern in string. The string is scanned left-to-right, and matches are returned in the order found. Empty matches are included in the result. See also the note about findall().\n",
    "#### match.group([group1, ...])\n",
    "Returns one or more subgroups of the match. If there is a single argument, the result is a single string; if there are multiple arguments, the result is a tuple with one item per argument. Without arguments, group1 defaults to zero (the whole match is returned). If a groupN argument is zero, the corresponding return value is the entire matching string; if it is in the inclusive range [1..99], it is the string matching the corresponding parenthesized group. If a group number is negative or larger than the number of groups defined in the pattern, an IndexError exception is raised. If a group is contained in a part of the pattern that did not match, the corresponding result is None. If a group is contained in a part of the pattern that matched multiple times, the last match is returned.\n",
    "#### get(key[, default])\n",
    "Return the value for key if key is in the dictionary, else default. If default is not given, it defaults to None, so that this method never raises a KeyError.\n",
    "#### setdefault(key[, default])\n",
    "If key is in the dictionary, return its value. If not, insert key with a value of default and return default. default defaults to None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'Spring'), (2, 'Summer'), (3, 'Fall'), (4, 'Winter')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons = ['Spring', 'Summer', 'Fall', 'Winter']\n",
    "list(enumerate(seasons))\n",
    "list(enumerate(seasons, start=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Isaac Newton'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = re.match(r\"(\\w+) (\\w+)\", \"Isaac Newton, physicist\")\n",
    "m.group(0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Isaac'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.group(1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Newton'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.group(2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mappings with flexible key lookup\n",
    "Sometimes it is convenient to have mappings that return some made-up value when a\n",
    "missing key is searched. There are two main approaches to this: one is to use a default\n",
    "dict instead of a plain dict. The other is to subclass dict or any other mapping type\n",
    "and add a __missing__ method.\n",
    "#### defaultdict: another take on missing keys\n",
    "A defaultdict is configured to create items on demand\n",
    "whenever a missing key is searched.\n",
    "Here is how it works: when instantiating a defaultdict, you provide a callable which\n",
    "is used to produce a default value whenever __getitem__ is passed a non-existent key\n",
    "argument.\n",
    "For example, given an empty defaultdict created as dd = defaultdict(list), if\n",
    "'new-key' is not in dd then the expression dd['new-key'] does the following steps:\n",
    "1. calls list() to create a new list;\n",
    "2. inserts the list into dd using 'new-key' as key;\n",
    "3. returns a reference to that list.\n",
    "The callable that produces the default values is held in an instance attribute called\n",
    "default_factory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [(1, 93)]\n",
      "after [(1, 71)]\n",
      "Although [(3, 1)]\n",
      "Amazon [(1, 14)]\n",
      "Apple [(1, 77)]\n",
      "became [(1, 29)]\n",
      "below [(3, 27)]\n",
      "briefly [(1, 21)]\n",
      "cap [(1, 114)]\n",
      "closed [(3, 20)]\n",
      "club [(3, 109)]\n",
      "company [(1, 63)]\n",
      "dollar [(3, 102)]\n",
      "how [(3, 67)]\n",
      "in [(1, 104)]\n",
      "it [(3, 51), (3, 79)]\n",
      "It [(3, 115)]\n",
      "joined [(3, 82)]\n",
      "magic [(3, 37)]\n",
      "market [(1, 107)]\n",
      "number [(3, 43)]\n",
      "On [(1, 2)]\n",
      "publicly [(1, 47)]\n",
      "quickly [(3, 71)]\n",
      "reach [(1, 86)]\n",
      "remarkable [(3, 56)]\n",
      "s [(3, 54)]\n",
      "second [(1, 40)]\n",
      "stock [(3, 14)]\n",
      "the [(1, 36), (3, 10), (3, 33), (3, 89)]\n",
      "to [(1, 83)]\n",
      "traded [(1, 56)]\n",
      "trillion [(1, 95), (3, 93)]\n",
      "Tuesday [(1, 5)]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import re\n",
    "import collections\n",
    "WORD_RE = re.compile('\\w+')\n",
    "index = collections.defaultdict(list)\n",
    "with open('zen.txt', encoding='utf-8') as fp:\n",
    "    for line_no, line in enumerate(fp, 1):\n",
    "        for match in WORD_RE.finditer(line):\n",
    "            word = match.group()\n",
    "            column_no = match.start()+1\n",
    "            location = (line_no, column_no)\n",
    "            index[word].append(location)\n",
    "# print in alphabetical order\n",
    "for word in sorted(index, key=str.upper):\n",
    "    print(word, index[word])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mechanism that makes defaultdict work by calling default_factory is actually\n",
    "the __missing__ special method, a feature supported by all standard mapping types\n",
    "that we discuss next.\n",
    "### The __missing__ method\n",
    "This method is not defined in the base dict class, but dict is aware of it: if you\n",
    "subclass dict and provide a __missing__ method, the standard dict.__getitem__ will\n",
    "call it whenever a key is not found, instead of raising KeyError."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class StrKeyDict0(dict):\n",
    "    def __missing__(self, key):\n",
    "        if isinstance(key, str):\n",
    "            raise KeyError(key)\n",
    "        return self[str(key)]\n",
    "    def get(self, key, default=None):\n",
    "        try:\n",
    "            return self[key]\n",
    "        except KeyError:\n",
    "            return default\n",
    "    def __contains__(self, key):\n",
    "        return key in self.keys() or str(key) in self.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'two'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = StrKeyDict0([('2', 'two'), ('4', 'four')])\n",
    "d['2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'two'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'1'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-6ee4d8336f71>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-9-a3d541be51ba>\u001b[0m in \u001b[0;36m__missing__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-9-a3d541be51ba>\u001b[0m in \u001b[0;36m__missing__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '1'"
     ]
    }
   ],
   "source": [
    "d[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'two'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.get(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'two'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.get('2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d.get('1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'n/a'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.get(1,'n/a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'2' in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 in d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### isinstance(object, classinfo)\n",
    "Return true if the object argument is an instance of the classinfo argument, or of a (direct, indirect or virtual) subclass thereof. If object is not an object of the given type, the function always returns false. If classinfo is a tuple of type objects (or recursively, other such tuples), return true if object is an instance of any of the types. If classinfo is not a type or tuple of types and such tuples, a TypeError exception is raised.\n",
    "## Variations of dict\n",
    "collections.OrderedDict\n",
    "maintains keys in insertion order, allowing iteration over items in a predictable\n",
    "order. The popitem method of an OrderedDict pops the first item by default, but\n",
    "if called as my_odict.popitem(last=True), it pops the last item added.\n",
    "collections.ChainMap\n",
    "holds a list of mappings which can be searched as one. The lookup is performed on\n",
    "each mapping in order, and succeeds if the key is found in any of them. This is useful\n",
    "to interpreters for languages with nested scopes, where each mapping represents a\n",
    "scope context.\n",
    "collections.Counter\n",
    "a mapping that holds an integer count for each key. Updating an existing key adds\n",
    "to its count. This can be used to count instances of hashable objects (the keys) or\n",
    "as a multiset — a set that can hold several occurrences of each element. Counter\n",
    "implements the + and - operators to combine tallies, and other useful methods such\n",
    "as most_common([n]), which returns an ordered list of tuples with the n most common\n",
    "items and their counts;\n",
    "collections.UserDict\n",
    "a pure Python implementation of a mapping that works like a standard dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'a': 5, 'b': 2, 'c': 1, 'd': 1, 'r': 2})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = collections.Counter('abracadabra')\n",
    "ct "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'a': 10, 'b': 2, 'c': 1, 'd': 1, 'r': 2, 'z': 3})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.update('aaaaazzz')\n",
    "ct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subclassing UserDict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "class StrKeyDict(collections.UserDict):\n",
    "    def __missing__(self, key):\n",
    "        if isinstance(key, str):\n",
    "            raise KeyError(key)\n",
    "        return self[str(key)]\n",
    "    def __contains__(self, key):\n",
    "        return str(key) in self.data\n",
    "    def __setitem__(self, key, item):\n",
    "        self.data[str(key)] = item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because UserDict subclasses MutableMapping, the remaining methods that make\n",
    "StrKeyDict a full-fledged mapping are inherited from UserDict, MutableMapping or\n",
    "Mapping. The latter have several useful concrete methods, in spite of being ABCs (abstract\n",
    "base classes). Worth noting:\n",
    "MutableMapping.update\n",
    "This powerful method can be called directly but is also used by __init__ to load\n",
    "the instance from other mappings, from iterables of (key, value) pairs and keyword\n",
    "arguments. Because it uses self[key] = value to add items, it ends up calling\n",
    "our implementation of __setitem__.\n",
    "Mapping.get\n",
    "## Immutable mappings\n",
    "Since Python 3.3 the types module provides a wrapper class MappingProxyType which,\n",
    "given a mapping, returns a mappingproxy instance that is a read-only but dynamic view\n",
    "of the original mapping. This means that updates to the original mapping can be seen\n",
    "in the mappingproxy, but changes cannot be made through it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mappingproxy({1: 'A'})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from types import MappingProxyType\n",
    "d={1:'A'}\n",
    "d_proxy=MappingProxyType(d)\n",
    "d_proxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_proxy[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'mappingproxy' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-6477fb19f299>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0md_proxy\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'mappingproxy' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "d_proxy[2]='b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'A', 2: 'b'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[2]='b'\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mappingproxy({1: 'A', 2: 'b'})"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_proxy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set theory\n",
    "A set is a collection of unique objects. \n",
    "Set elements must be hashable. The set type is not hashable, but frozenset is, so you\n",
    "can have frozenset elements inside a set.\n",
    "In addition to guaranteeing uniqueness, the set types implement the essential set operations\n",
    "as infix operators, so, given two sets a and b, a | b returns their union, a & b\n",
    "computes the intersection, and a - b the difference. Smart use of set operations can\n",
    "reduce both the line count and the run time of Python programs, at the same time\n",
    "making code easier to read and reason about — by removing loops and lots of conditional\n",
    "logic.\n",
    "#### set literals\n",
    "The syntax of set literals — {1}, {1, 2}, etc. — looks exactly like the math notation,\n",
    "with one important exception: there’s no literal notation for the empty set, we must\n",
    "remember to write set().\n",
    "Don’t forget: to create an empty set, use the constructor without an\n",
    "argument: set(). If you write {}, you’re creating an empty dict —\n",
    "this hasn’t changed.\n",
    "Literal set syntax like {1, 2, 3} is both faster and more readable than calling the\n",
    "constructor e.g. set([1, 2, 3]). The latter form is slower because, to evaluate it, Python\n",
    "has to look up the set name to fetch the constructor, then build a list and finally pass\n",
    "it to the constructor. In contrast, to process the a literal like {1, 2, 3}, Python runs a\n",
    "specialized BUILD_SET bytecode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eggs', 'spam'}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = ['spam', 'spam', 'eggs', 'spam']\n",
    "set(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['eggs', 'spam']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s={1}\n",
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1           0 LOAD_CONST               0 (1)\n",
      "              2 BUILD_SET                1\n",
      "              4 RETURN_VALUE\n"
     ]
    }
   ],
   "source": [
    "from dis import dis\n",
    "dis('{1}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1           0 LOAD_NAME                0 (set)\n",
      "              2 LOAD_CONST               0 (1)\n",
      "              4 BUILD_LIST               1\n",
      "              6 CALL_FUNCTION            1\n",
      "              8 RETURN_VALUE\n"
     ]
    }
   ],
   "source": [
    "dis('set([1])')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no special syntax to represent frozenset literals — they must be created by\n",
    "calling the constructor. The standard string representation in Python 3 looks like a\n",
    "frozenset constructor call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frozenset({0, 1, 2, 3, 4, 5, 6, 7, 8, 9})"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frozenset(range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### set comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'#',\n",
       " '$',\n",
       " '%',\n",
       " '+',\n",
       " '<',\n",
       " '=',\n",
       " '>',\n",
       " '¢',\n",
       " '£',\n",
       " '¤',\n",
       " '¥',\n",
       " '§',\n",
       " '©',\n",
       " '¬',\n",
       " '®',\n",
       " '°',\n",
       " '±',\n",
       " 'µ',\n",
       " '¶',\n",
       " '×',\n",
       " '÷'}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from unicodedata import name\n",
    "{chr(i) for i in range(32, 256) if 'SIGN' in name(chr(i),'')}"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set operations\n",
    "![image.png](attachment:image.png)\n",
    "## dict and set under the hood\n",
    "Understanding how Python dictionaries and sets are implemented using hash tables is\n",
    "helpful to make sense of their strengths and limitations.\n",
    "Here are some questions this section will answer:\n",
    "• How efficient are Python dict and set?\n",
    "• Why are they unordered?\n",
    "• Why can’t we use any Python object as a dict key or set element?\n",
    "• Why does the order of the dict keys or set elements depend on insertion order,\n",
    "and may change during the lifetime of the structure?\n",
    "• Why is it bad to add items to a dict or set while iterating through it?\n",
    "#### A performance experiment\n",
    "If your program does any kind of I/O, the lookup time for keys in dicts or sets is negligible,\n",
    "regardless of the dict or set size (as long as it does fit in RAM).\n",
    "#### Hash tables in dictionaries\n",
    "A hash table is a sparse array, i.e. an array which always has empty cells. In standard\n",
    "data structure texts, the cells in a hash table are often called “buckets”. In a dict hash\n",
    "table, there is a bucket for each item, and it contains two fields: a reference to the key\n",
    "and a reference to the value of the item. Because all buckets have the same size, access\n",
    "to an individual bucket is done by offset.\n",
    "Python tries to keep at least 1/3 of the buckets empty; if the hash table becomes too\n",
    "crowded, it is copied to a new location with room for more buckets.\n",
    "To put an item in a hash table the first step is to calculate the hash value of the item key,\n",
    "which is done with the hash() built-in function\n",
    "##### Hashes and equality\n",
    "The hash() built-in function works directly with built-in types and falls back to calling\n",
    "__hash__ for user-defined types. If two objects compare equal, their hash values must\n",
    "also be equal, otherwise the hash table algorithm does not work. For example, because\n",
    "1 == 1.0 is true, hash(1) == hash(1.0) must also be true, even though the internal\n",
    "representation of an int and a float are very different7.\n",
    "##### The hash table algorithm\n",
    "To fetch the value at my_dict[search_key], Python calls hash(search_key) to obtain\n",
    "the hash value of search_key and uses the least significant bits of that number as an\n",
    "offset to look up a bucket in the hash table (the number of bits used depends on the\n",
    "current size of the table). If the found bucket is empty, KeyError is raised. Otherwise,\n",
    "the found bucket has an item — a found_key:found_value pair — and then Python\n",
    "checks whether search_key == found_key. If they match, that was the item sought:\n",
    "found_value is returned.\n",
    "However, if search_key and found_key do not match, this is a hash collision. This happens\n",
    "because a hash function maps arbitrary objects to a small number of bits, and —\n",
    "in addition — the hash table is indexed with a subset of those bits. To resolve the collision,\n",
    "the algorithm then takes different bits in the hash, massages them in a particular\n",
    "way and uses the result as an offset to look up a different bucket8. If that is empty,\n",
    "KeyError is raised; if not, either the keys match and the item value is returned, or the\n",
    "collision resolution process is repeated.\n",
    "![image.png](attachment:image.png)\n",
    "The process to insert or update an item is the same, except that when an empty bucket\n",
    "is located, the new item is put there, and when a bucket with a matching key is found,\n",
    "the value in that bucket is overwritten with the new value.\n",
    "Additionally, when inserting items Python may determine that the hash table is too\n",
    "crowded and rebuild it to a new location with more room. As the hash table grows, so\n",
    "does the number of hash bits used as bucket offsets, and this keeps the rate of collisions\n",
    "low.\n",
    "### Practical consequences of how dict works\n",
    "#1: Keys must be hashable objects\n",
    "An object is hashable if all of these requirements are met:\n",
    "1. It supports the hash() function via a __hash__() method that always returns the\n",
    "same value over the lifetime of the object.\n",
    "2. It supports equality via an __eq__() method.\n",
    "3. If a == b is True then hash(a) == hash(b) must also be True.\n",
    "\n",
    "User-defined types are hashable by default because their hash value is their id() and\n",
    "they all compare not equal.\n",
    "\n",
    "#2: dicts have significant memory overhead\n",
    "Because a dict uses a hash table internally, and hash tables must be sparse to work, they\n",
    "are not space efficient. For example, if you are handling a large quantity of records it\n",
    "makes sense to store them in a list of tuples or named tuples instead of using a list of\n",
    "dictionaries in JSON style, with one dict per record. Replacing dicts with tuples reduces\n",
    "the memory usage in two ways: by removing the overhead of one hash table per record\n",
    "and by not storing the field names again with each record.\n",
    "\n",
    "#3: Key search is very fast\n",
    "The dict implementation is an example of trading space for time: dictionaries have\n",
    "significant memory overhead, but they provide fast access regardless of the size of the\n",
    "dictionary — as long as it fits in memory.\n",
    "\n",
    "#4: Key ordering depends on insertion order\n",
    "When a hash collision happens, the second key ends up in a position that it would not\n",
    "normally occupy if it had been inserted first. So, a dict built as dict([(key1, value1),\n",
    "(key2, value2)]) compares equal to dict([(key2, value2), (key1, value1)]),\n",
    "but their key ordering may not be the same if the hashes of key1 and key2 collide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d1: dict_keys([86, 91, 1, 62, 55, 92, 880, 234, 7, 81])\n",
      "d2: dict_keys([1, 7, 55, 62, 81, 86, 91, 92, 234, 880])\n",
      "d3: dict_keys([880, 55, 86, 91, 62, 81, 234, 92, 7, 1])\n"
     ]
    }
   ],
   "source": [
    "DIAL_CODES = [\n",
    "(86, 'China'),\n",
    "(91, 'India'),\n",
    "(1, 'United States'),\n",
    "(62, 'Indonesia'),\n",
    "(55, 'Brazil'),\n",
    "(92, 'Pakistan'),\n",
    "(880, 'Bangladesh'),\n",
    "(234, 'Nigeria'),\n",
    "(7, 'Russia'),\n",
    "(81, 'Japan'),\n",
    "]\n",
    "d1 = dict(DIAL_CODES)\n",
    "print('d1:', d1.keys())\n",
    "d2 = dict(sorted(DIAL_CODES))\n",
    "print('d2:', d2.keys())\n",
    "d3 = dict(sorted(DIAL_CODES, key=lambda x:x[1]))\n",
    "print('d3:', d3.keys())\n",
    "assert d1 == d2 and d2 == d3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### lambda\n",
    "An anonymous inline function consisting of a single expression which is evaluated when the function is called. The syntax to create a lambda function is lambda [parameters]: expression\n",
    "#5: Adding items to a dict may change the order of existing keys\n",
    "Whenever you add a new item to a dict, the Python interpreter may decide that the\n",
    "hash table of that dictionary needs to grow. This entails building a new, bigger hash\n",
    "table, and adding all current items to the new table. During this process, new (but\n",
    "different) hash collisions may happen, with the result that the keys are likely to be ordered\n",
    "differently in the new hash table. All of this is implementation-dependent, so you\n",
    "cannot reliably predict when it will happen. If you are iterating over the dictionay keys\n",
    "and changing them at the same time, your loop may not scan all the items as expected\n",
    "— not even the items that were already in the dictionary before you added to it.\n",
    "This is why modifying the contents of a dict while iterating through it is a bad idea. If\n",
    "you need to scan and add items to a dictionary, do it in two steps: read the dict from\n",
    "start to finish and collect the needed additions in a second dict. Then update the first\n",
    "one with it.\n",
    "## How sets work — practical consequences\n",
    "The set and frozenset types are also implemented with a hash table, except that each\n",
    "bucket holds only a reference to the element (as if it were a key in a dict, but without\n",
    "a value to go with it). In fact, before set was added to the language, we often used\n",
    "dictionaries with dummy values just to perform fast membership tests on the keys.\n",
    "Everything said in “Practical consequences of how dict works” on page 90 about how\n",
    "the underlying hash table determines the behavior of a dict applies to a set. Without\n",
    "repeating the previous section, we can summarize it for sets with just a few words:\n",
    "1. Set elements must be hashable objects.\n",
    "2. Sets have a significant memory overhead.\n",
    "3. Membership testing is very efficient.\n",
    "4. Element ordering depends on insertion order.\n",
    "5. Adding elements to a set may change the order of other elements.\n",
    "\n",
    "# CHAPTER 4 Text versus bytes\n",
    "## Character issues\n",
    "The concept of “string” is simple enough: a string is a sequence of characters. The problem\n",
    "lies in the definition of “character”.\n",
    "In 2014 the best definition of “character” we have is a Unicode character. Accordingly,\n",
    "the items you get out of a Python 3 str are Unicode characters, just like the items of a\n",
    "unicode object in Python 2 — and not the raw bytes you get from a Python 2 str.\n",
    "The Unicode standard explicitly separates the identity of characters from specific byte\n",
    "representations.\n",
    "• The identity of a character — its code point — is a number from 0 to 1,114,111 (base\n",
    "10), shown in the Unicode standard as 4 to 6 hexadecimal digits with a “U+” prefix.\n",
    "For example, the code point for the letter A is U+0041, the Euro sign is U+20AC\n",
    "and the musical symbol G clef is assigned to code point U+1D11E. About 10% of\n",
    "the valid code points have characters assigned to them in Unicode 6.3, the standard\n",
    "used in Python 3.4.\n",
    "• The actual bytes that represent a character depend on the encoding in use. An encoding\n",
    "is an algorithm that converts code points to byte sequences and vice-versa.\n",
    "The code point for A (U+0041) is encoded as the single byte \\x41 in the UTF-8\n",
    "encoding, or as the bytes \\x41\\x00 in UTF-16LE encoding. As another example,\n",
    "the Euro sign (U+20AC) becomes three bytes in UTF-8 — \\xe2\\x82\\xac — but in\n",
    "UTF-16LE it is encoded as two bytes: \\xac\\x20.\n",
    "Converting from code points to bytes is encoding; from bytes to code points is decoding.\n",
    "\n",
    "If you need a memory aid to distinguish .decode() from .en\n",
    "code(), convince yourself that byte sequences can be cryptic machine\n",
    "core dumps while Unicode str objects are “human” text .\n",
    "Therefore, it makes sense that we decode bytes to str to get human\n",
    "readable text, and we encode str to bytes for storage or transmission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'café'\n",
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'caf\\xc3\\xa9'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=s.encode('utf-8')\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'café'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.decode('utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Byte essentials\n",
    "The new binary sequence types are unlike the Python 2 str in many regards. The first\n",
    "thing to know is that there are two basic built-in types for binary sequences: the immutable\n",
    "bytes type introduced in Python 3 and the mutable bytearray, added in\n",
    "Python 2.62.\n",
    "Each item in bytes or bytearray is an integer from 0 to 255, and not a 1-character\n",
    "string like in the Python 2 str. However a slice of a binary sequence always produces a\n",
    "binary sequence of the same type — including slices of length 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cafe = bytes('café', encoding='utf_8')\n",
    "cafe[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'c'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cafe[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bytearray(b'caf\\xc3\\xa9')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca= bytearray(cafe)\n",
    "ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bytearray(b'\\xa9')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cafe[-1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although binary sequences are really sequences of integers, their literal notation reflects\n",
    "the fact that ASCII text is often embedded in them. Therefore, three different displays\n",
    "are used, depending on each byte value:\n",
    "• For bytes in the printable ASCII range — from space to ~ — the ASCII character\n",
    "itself is used.\n",
    "• For bytes corresponding to tab, newline, carriage return and \\, the escape sequences\n",
    "\\t, \\n, \\r and \\\\ are used.\n",
    "• For every other byte value, an hexadecimal escape sequence is used, e.g. \\x00 is the\n",
    "null byte.\n",
    "Both bytes and bytearray support every str method except those that do formatting\n",
    "(format, format_map) and a few others that depend on Unicode data: casefold, isdec\n",
    "imal, isidentifier, isnumeric, isprintable and encode. This means that you can\n",
    "use familiar string methods like endswith, replace, strip, translate, upper and dozens\n",
    "of others with binary sequences — only using bytes and not str arguments. In\n",
    "addition, the regular expression functions in the re module also work on binary se‐\n",
    "quences, if the regex is compiled from a binary sequence instead of a str.\n",
    "\n",
    "Binary sequences have a class method that str doesn’t have: fromhex, which builds a\n",
    "binary sequence by parsing pairs of hex digits optionally separated by space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'1K\\xce\\xa9'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bytes.fromhex('31 4B CE A9')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other ways of building bytes or bytearray instances are calling their constructors\n",
    "with:\n",
    "• a str and an encoding keyword argument.\n",
    "• an iterable providing items with values from 0 to 255.\n",
    "• a single integer, to create a binary sequence of that size initialized with null bytes3.\n",
    "• an object that implements the buffer protocol (eg. bytes, bytearray, memoryview,\n",
    "array.array); this copies the bytes from the source object to the newly created\n",
    "binary sequence.\n",
    "Building a binary sequence from a buffer-like object is a low-level operation that may\n",
    "involve type casting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\xfe\\xff\\xff\\xff\\x00\\x00\\x01\\x00\\x02\\x00'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import array\n",
    "numbers = array.array('h', [-2, -1, 0, 1, 2])\n",
    "octets = bytes(numbers)\n",
    "octets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Structs and memory views\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import struct\n",
    "fmt = '<3s3sHH' #\n",
    "with open('image001.png', 'rb') as fp:\n",
    "    img = memoryview(fp.read()) #\n",
    "header = img[:10] #\n",
    "bytes(header) #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(b'\\x89PN', b'G\\r\\n', 2586, 0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "struct.unpack(fmt, header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic encoders/decoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "latin_1\tb'El Ni\\xf1o'\n",
      "utf_8\tb'El Ni\\xc3\\xb1o'\n",
      "utf_16\tb'\\xff\\xfeE\\x00l\\x00 \\x00N\\x00i\\x00\\xf1\\x00o\\x00'\n"
     ]
    }
   ],
   "source": [
    "for codec in ['latin_1', 'utf_8', 'utf_16']:\n",
    "    print(codec, 'El Niño'.encode(codec), sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some encodings, like ASCII and even the\n",
    "multi-byte GB2312, cannot represent every Unicode character. The UTF encodings,\n",
    "however, are designed to handle every Unicode code point.\n",
    "\n",
    "## Understanding encode/decode problems\n",
    "### Coping with UnicodeEncodeError\n",
    "Most non-UTF codecs handle only a small subset of the Unicode characters. When\n",
    "converting text to bytes, if a character is not defined in the target encoding, UnicodeEn\n",
    "codeError will be raised, unless special handling is provided by passing an errors\n",
    "argument to the encoding method or function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'S\\xc3\\xa3o Paulo'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city = 'São Paulo'\n",
    "city.encode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\xff\\xfeS\\x00\\xe3\\x00o\\x00 \\x00P\\x00a\\x00u\\x00l\\x00o\\x00'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city.encode('utf_16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'S\\xe3o Paulo'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city.encode('iso8859_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeEncodeError",
     "evalue": "'charmap' codec can't encode character '\\xe3' in position 1: character maps to <undefined>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnicodeEncodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-768485688c3d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cp437'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Anaconda3\\lib\\encodings\\cp437.py\u001b[0m in \u001b[0;36mencode\u001b[1;34m(self, input, errors)\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'strict'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharmap_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mencoding_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'strict'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnicodeEncodeError\u001b[0m: 'charmap' codec can't encode character '\\xe3' in position 1: character maps to <undefined>"
     ]
    }
   ],
   "source": [
    "city.encode('cp437')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'So Paulo'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city.encode('cp437', errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'S?o Paulo'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city.encode('cp437', errors='replace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'S&#227;o Paulo'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city.encode('cp437', errors='xmlcharrefreplace')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coping with UnicodeDecodeError\n",
    "Not every byte holds a valid ASCII character, and not every byte sequence is valid UTF-8\n",
    "or UTF-16, therefore when you assume one of these encodings while converting a binary\n",
    "sequence to text, you will get a UnicodeDecodeError if unexpected bytes are found.\n",
    "\n",
    "On the other hand, many legacy 8-bit encodings like 'cp1252', 'iso8859_1', 'koi8_r'\n",
    "are able to decode any stream of bytes, including random noise, without generating\n",
    "errors. Therefore, if your program assumes the wrong 8-bit encoding, it will silently\n",
    "decode garbage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Montréal'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "octets = b'Montr\\xe9al'\n",
    "octets.decode('cp1252')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Montrιal'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "octets.decode('iso8859_7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MontrИal'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "octets.decode('koi8_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0xe9 in position 5: invalid continuation byte",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-f3a91f0d51e5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0moctets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'utf_8'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 5: invalid continuation byte"
     ]
    }
   ],
   "source": [
    "octets.decode('utf_8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Montr�al'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "octets.decode('utf_8',errors='replace')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SyntaxError when loading modules with unexpected encoding\n",
    "UTF-8 is the default source encoding for Python 3, just as ASCII was the default for\n",
    "Python 2 (starting with 2.5).\n",
    "### How to discover the encoding of a byte sequence\n",
    "Short answer: you can’t. You must be told.\n",
    "### BOM: a useful gremlin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\xff\\xfeE\\x00l\\x00 \\x00N\\x00i\\x00\\xf1\\x00o\\x00'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u16 = 'El Niño'.encode('utf_16')\n",
    "u16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bytes are: b'\\xff\\xfe'. That is a BOM — byte-order mark — denoting the “littleendian”\n",
    "byte ordering of the Intel CPU where the encoding was performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[255, 254, 69, 0, 108, 0, 32, 0, 78, 0, 105, 0, 241, 0, 111, 0]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(u16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[69, 0, 108, 0, 32, 0, 78, 0, 105, 0, 241, 0, 111, 0]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u16le = 'El Niño'.encode('utf_16le')\n",
    "list(u16le)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 69, 0, 108, 0, 32, 0, 78, 0, 105, 0, 241, 0, 111]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u16be = 'El Niño'.encode('utf_16be')\n",
    "list(u16be)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling text files\n",
    "![image.png](attachment:image.png)\n",
    "The best practice for handling text is the “Unicode sandwich” (Figure 4-2)8. This means\n",
    "that bytes should be decoded to str as early as possible on input,\n",
    "On output, the str are encoded to bytes as\n",
    "late as possible.\n",
    "Python 3 makes it easier to follow the advice of the Unicode sandwich, because the open\n",
    "built-in does the necessary decoding when reading and encoding when writing files in\n",
    "text mode, so all you get from my_file.read() and pass to my_file.write(text) are\n",
    "str objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp = open('cafe.txt', 'w',encoding='utf-8').write('café')\n",
    "fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'caf茅'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open('cafe.txt').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp = open('cafe.txt', 'w').write('café')\n",
    "fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'café'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open('cafe.txt').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='cafe.txt' mode='w' encoding='utf_8'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp = open('cafe.txt', 'w', encoding='utf_8')\n",
    "fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fp.write('café')\n",
    "fp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.stat('cafe.txt').st_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='cafe.txt' mode='r' encoding='cp936'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp2 = open('cafe.txt')\n",
    "fp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cp936'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp2.encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'caf茅'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp2.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='cafe.txt' mode='r' encoding='utf_8'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp3 = open('cafe.txt', encoding='utf_8')\n",
    "fp3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'café'"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp3.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.BufferedReader name='cafe.txt'>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp4 = open('cafe.txt', 'rb')\n",
    "fp4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'caf\\xc3\\xa9'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp4.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do not open text files in binary mode unless you need to analyze the\n",
    "file contents to determine the encoding — even then, you should be\n",
    "using Chardet instead of reinventing the wheel. Ordinary code\n",
    "should only use binary mode to open binary files, like raster images.\n",
    "### Encoding defaults: a madhouse\n",
    "Several settings affect the encoding defaults for I/O in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " locale.getpreferredencoding() -> 'cp936'\n",
      "                 type(my_file) -> <class '_io.TextIOWrapper'>\n",
      "              my_file.encoding -> 'cp936'\n",
      "           sys.stdout.isatty() -> False\n",
      "           sys.stdout.encoding -> 'UTF-8'\n",
      "            sys.stdin.isatty() -> False\n",
      "            sys.stdin.encoding -> 'cp936'\n",
      "           sys.stderr.isatty() -> False\n",
      "           sys.stderr.encoding -> 'UTF-8'\n",
      "      sys.getdefaultencoding() -> 'utf-8'\n",
      "   sys.getfilesystemencoding() -> 'utf-8'\n"
     ]
    }
   ],
   "source": [
    "import sys, locale\n",
    "expressions = \"\"\"\n",
    "    locale.getpreferredencoding()\n",
    "    type(my_file)\n",
    "    my_file.encoding\n",
    "    sys.stdout.isatty()\n",
    "    sys.stdout.encoding\n",
    "    sys.stdin.isatty()\n",
    "    sys.stdin.encoding\n",
    "    sys.stderr.isatty()\n",
    "    sys.stderr.encoding\n",
    "    sys.getdefaultencoding()\n",
    "    sys.getfilesystemencoding()\n",
    "\"\"\"\n",
    "my_file = open('dummy', 'w')\n",
    "for expression in expressions.split():\n",
    "    value =eval(expression)\n",
    "    print(expression.rjust(30), '->',repr(value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### eval(expression, globals=None, locals=None)\n",
    "The arguments are a string and optional globals and locals. If provided, globals must be a dictionary. If provided, locals can be any mapping object.\n",
    "\n",
    "The expression argument is parsed and evaluated as a Python expression (technically speaking, a condition list) using the globals and locals dictionaries as global and local namespace. If the globals dictionary is present and does not contain a value for the key __builtins__, a reference to the dictionary of the built-in module builtins is inserted under that key before expression is parsed. This means that expression normally has full access to the standard builtins module and restricted environments are propagated. If the locals dictionary is omitted it defaults to the globals dictionary. If both dictionaries are omitted, the expression is executed in the environment where eval() is called. The return value is the result of the evaluated expression. Syntax errors are reported as exceptions. Example\n",
    "\n",
    "#### repr(object)\n",
    "Return a string containing a printable representation of an object. For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval(), otherwise the representation is a string enclosed in angle brackets that contains the name of the type of the object together with additional information often including the name and address of the object. A class can control what this function returns for its instances by defining a __repr__() method.\n",
    "----------------------\n",
    "• If you omit the encoding argument when opening a file, the default is given by\n",
    "locale.getpreferredencoding() ('cp1252' in Example 4-12).\n",
    "• The encoding of sys.stdout/stdin/stderr is given by the https://docs.python.org/\n",
    "3/using/cmdline.html#envvar-PYTHONIOENCODING [PYTHONIOENCODING] environment\n",
    "variable, if present, otherwise it is either inherited from the console or\n",
    "defined by locale.getpreferredencoding() if the output/input is redirected to/\n",
    "from a file.\n",
    "• sys.getdefaultencoding() is used internally by Python to convert binary data to/\n",
    "from str; this happens less often in Python 3, but still happens10. Changing this\n",
    "setting is not supported11.\n",
    "• sys.getfilesystemencoding() is used to encode/decode file names (not file contents).\n",
    "It is used when open() gets a str argument for the file name; if the file name\n",
    "is given as a bytes argument, it is passed unchanged to the OS API. The Python\n",
    "Unicode HOWTO says: “on Windows, Python uses the name mbcs to refer to whatever\n",
    "the currently configured encoding is.” The acronym MBCS stands for Multi\n",
    "Byte Character Set, which for Microsoft are the legacy variable-width encodings like gb2312 or Shift_JIS, but not UTF-8footnote\n",
    "\n",
    "To summarize, the most important encoding setting is that returned by locale.get\n",
    "preferredencoding(): it is the default for opening text files and for sys.stdout/stdin/\n",
    "stderr when they are redirected to files. However, the documentation reads (in part):\n",
    "locale.getpreferredencoding(do_setlocale=True)\n",
    "Return the encoding used for text data, according to user preferences. User preferences\n",
    "are expressed differently on different systems, and might not be available programmatically\n",
    "on some systems, so this function only returns a guess. […]\n",
    "Therefore, the best advice about encoding defaults is: do not rely on them.\n",
    "If you follow the advice of the Unicode sandwich and always are explicit about the\n",
    "encodings in your programs, you will avoid a lot of pain.\n",
    "\n",
    "## Normalizing Unicode for saner comparisons\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('café', 'café')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = 'café'\n",
    "s2 = 'cafe\\u0301'\n",
    "s1,s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 5)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s1),len(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1==s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code point U+0301 is the COMBINING ACUTE ACCENT. Using it after “e” renders “é”.\n",
    "In the Unicode standard, sequences like 'é' and 'e\\u0301' are called “canonical equivalents”,\n",
    "and applications are supposed to treat them as the same. But Python sees two\n",
    "different sequences of code points, and considers them not equal.\n",
    "The solution is to use Unicode normalization, provided by the unicodedata.normal\n",
    "ize function. The first argument to that function is one of four strings: 'NFC', 'NFD',\n",
    "'NFKC' and 'NFKD'. Let’s start with the first two.\n",
    "NFC (Normalization Form C) composes the code points to produce the shortest equivalent\n",
    "string, while NFD decomposes, expanding composed characters into base characters\n",
    "and separate combining characters. Both of these normalizations make comparisons\n",
    "work as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from unicodedata import normalize\n",
    "s1 = 'café'\n",
    "s2 = 'cafe\\u0301'\n",
    "len(normalize('NFC', s1)), len(normalize('NFC', s2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('café', 'café')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1,s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(normalize('NFD', s1)), len(normalize('NFD', s2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize('NFC', s1) == normalize('NFC', s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'café'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize('NFC', s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'café'"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize('NFD', s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize('NFD', s1) == normalize('NFD', s2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Western keyboards usually generate composed characters, so text typed by users will be\n",
    "in NFC by default. But to be safe it may be good to sanitize strings with normal\n",
    "ize('NFC', user_text) before saving. NFC is also the normalization form recommended\n",
    "by the W3C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OHM SIGN'"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from unicodedata import normalize, name\n",
    "ohm = '\\u2126'\n",
    "name(ohm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GREEK CAPITAL LETTER OMEGA'"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohm_c = normalize('NFC', ohm)\n",
    "name(ohm_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohm == ohm_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize('NFC', ohm) == normalize('NFC', ohm_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The letter K in the acronym for the other two normalization forms — NFKC and\n",
    "NFKD — stands for “compatibility”. These are stronger forms of normalization, affecting\n",
    "the so called “compatibility characters”. Although one goal of Unicode is to have a\n",
    "single “canonical” code point for each character, some characters appear more than once\n",
    "for compatibility with preexisting standards.\n",
    "\n",
    "NFKC and NFKD normalization should be applied with care and\n",
    "only in special cases — e.g. search and indexing — and not for permanent\n",
    "storage, as these transformations cause data loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1⁄2'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half = '½'\n",
    "normalize('NFKC', half)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'42'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "four_squared = '4²'\n",
    "normalize('NFKC', four_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('μ', 'μ')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "micro = 'μ'\n",
    "micro_kc = normalize('NFKC', micro)\n",
    "micro, micro_kc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(956, 956)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord(micro), ord(micro_kc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('GREEK SMALL LETTER MU', 'GREEK SMALL LETTER MU')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name(micro), name(micro_kc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case folding\n",
    "Case folding is essentially converting all text to lowercase, with some additional transformations.\n",
    "It is supported by the str.casefold() method (new in Python 3.3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GREEK SMALL LETTER MU'"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "micro = 'μ'\n",
    "name(micro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GREEK SMALL LETTER MU'"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "micro_cf = micro.casefold()\n",
    "name(micro_cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('μ', 'μ')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "micro,micro_cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eszett = 'ß'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'LATIN SMALL LETTER SHARP S'"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name(eszett)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eszett_cf = eszett.casefold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ß', 'ss')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eszett,eszett_cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of Python 3.4 there are 116 code points for which str.casefold() and str.low\n",
    "er() return different results. That’s 0.11% of a total of 110,122 named characters in\n",
    "Unicode 6.3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility functions for normalized text matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from unicodedata import normalize\n",
    "def nfc_equal(str1, str2):\n",
    "    return normalize('NFC', str1) == normalize('NFC', str2)\n",
    "def fold_equal(str1, str2):\n",
    "    return (normalize('NFC', str1).casefold() ==normalize('NFC', str2).casefold())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = 'café'\n",
    "s2 = 'cafe\\u0301'\n",
    "s1 == s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfc_equal(s1, s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfc_equal('A', 'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3 = 'Straße'\n",
    "s4 = 'strasse'\n",
    "s3==s4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfc_equal(s3, s4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fold_equal(s3, s4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fold_equal(s1, s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fold_equal('A', 'a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extreme “normalization”: taking out diacritics\n",
    "unicodedata.combining(chr)\n",
    "Returns the canonical combining class assigned to the character chr as integer. Returns 0 if no combining class is defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import unicodedata\n",
    "import string\n",
    "def shave_marks(txt):\n",
    "    \"\"\"Remove all diacritic marks\"\"\"\n",
    "    norm_txt = unicodedata.normalize('NFD', txt)\n",
    "    shaved = ''.join(c for c in norm_txt    if not unicodedata.combining(c))\n",
    "    return unicodedata.normalize('NFC', shaved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'“Herr Voß: • ½ cup of OEtker™ caffe latte • bowl of acai.”'"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order = '“Herr Voß: • ½ cup of OEtker™ caffè latte • bowl of açaí.”'\n",
    "shave_marks(order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ζεφυρος, Zefiro'"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Greek = 'Ζέφυρος, Zéfiro'\n",
    "shave_marks(Greek)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function shave_marks from Example 4-14 works all right, but maybe it goes too\n",
    "far. Often the reason to remove diacritics is to change Latin text to pure ASCII, but\n",
    "shave_marks also changes non-Latin characters — like Greek letters — which will never\n",
    "become ASCII just by losing their accents. So it makes sense to analyze each base character\n",
    "and to remove attached marks only if the base character is a letter from the Latin\n",
    "alphabet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def shave_marks_latin(txt):\n",
    "    \"\"\"Remove all diacritic marks from Latin base characters\"\"\"\n",
    "    norm_txt = unicodedata.normalize('NFD', txt)\n",
    "    latin_base = False\n",
    "    keepers = []\n",
    "    for c in norm_txt:\n",
    "        if unicodedata.combining(c) and latin_base:\n",
    "            continue # ignore diacritic on Latin base char\n",
    "        keepers.append(c)\n",
    "        # if it isn't combining char, it's a new base char\n",
    "        if not unicodedata.combining(c):\n",
    "            latin_base = c in string.ascii_letters\n",
    "    shaved = ''.join(keepers)\n",
    "    return unicodedata.normalize('NFC', shaved)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting Unicode text\n",
    "The standard way to sort non-ASCII text in Python is to use the locale.strxfrm\n",
    "function which, according to the locale module docs, “transforms a string to one that\n",
    "can be used in locale-aware comparisons”.\n",
    "To enable locale.strxfrm you must first set a suitable locale for your application, and\n",
    "pray that the OS supports it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['acerola', 'atemoia', 'açaí', 'caju', 'cajá']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits = ['caju', 'atemoia', 'cajá', 'açaí', 'acerola']\n",
    "sorted(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['açaí', 'acerola', 'atemoia', 'cajá', 'caju']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import locale\n",
    "locale.setlocale(locale.LC_COLLATE, 'English_United States.850')\n",
    "sorted_fruits = sorted(fruits, key=locale.strxfrm)\n",
    "sorted_fruits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sorting with the Unicode Collation Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pyuca'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-19cbf0f40cb5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpyuca\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mcoll\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpyuca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCollator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfruits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'caju'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'atemoia'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'cajá'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'açaí'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'acerola'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msorted_fruits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfruits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcoll\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msorted_fruits\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pyuca'"
     ]
    }
   ],
   "source": [
    "import pyuca\n",
    "coll = pyuca.Collator()\n",
    "fruits = ['caju', 'atemoia', 'cajá', 'açaí', 'acerola']\n",
    "sorted_fruits = sorted(fruits, key=coll.sort_key)\n",
    "sorted_fruits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Unicode database\n",
    "The Unicode standard provides an entire database — in the form of numerous structured\n",
    "text files — that includes not only the table mapping code points to character\n",
    "names, but also lot of metadata about the individual characters and how they are related.\n",
    "For example, the Unicode database records whether a character is printable, is a letter,\n",
    "is a decimal digit or is some other numeric symbol. That’s how the str methods isi\n",
    "dentifier, isprintable, isdecimal and isnumeric work. str.casefold also uses information\n",
    "from a Unicode table.\n",
    "The unicodedata module has functions that return character metadata, for instance,\n",
    "its official name in the standard, whether it is a combining character (e.g. diacritic like\n",
    "a combining tilde) and the numeric value of the symbol for humans (not its code point)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U+0031\t  1   \tre_dig\tisdig\tisnum\t 1.00\tDIGIT ONE\n",
      "U+00bc\t  ¼   \t-\t-\tisnum\t 0.25\tVULGAR FRACTION ONE QUARTER\n",
      "U+00b2\t  ²   \t-\tisdig\tisnum\t 2.00\tSUPERSCRIPT TWO\n",
      "U+0969\t  ३   \tre_dig\tisdig\tisnum\t 3.00\tDEVANAGARI DIGIT THREE\n",
      "U+136b\t  ፫   \t-\tisdig\tisnum\t 3.00\tETHIOPIC DIGIT THREE\n",
      "U+216b\t  Ⅻ   \t-\t-\tisnum\t12.00\tROMAN NUMERAL TWELVE\n",
      "U+2466\t  ⑦   \t-\tisdig\tisnum\t 7.00\tCIRCLED DIGIT SEVEN\n",
      "U+2480\t  ⒀   \t-\t-\tisnum\t13.00\tPARENTHESIZED NUMBER THIRTEEN\n",
      "U+3285\t  ㊅   \t-\t-\tisnum\t 6.00\tCIRCLED IDEOGRAPH SIX\n"
     ]
    }
   ],
   "source": [
    "import unicodedata\n",
    "import re\n",
    "re_digit = re.compile(r'\\d')\n",
    "sample = '1\\xbc\\xb2\\u0969\\u136b\\u216b\\u2466\\u2480\\u3285'\n",
    "for char in sample:\n",
    "    print('U+%04x' % ord(char),\n",
    "    char.center(6),\n",
    "    're_dig' if re_digit.match(char) else '-',\n",
    "    'isdig' if char.isdigit() else '-',\n",
    "    'isnum' if char.isnumeric() else '-',\n",
    "    format(unicodedata.numeric(char), '5.2f'),\n",
    "    unicodedata.name(char),\n",
    "    sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dual mode str and bytes APIs\n",
    "#### str versus bytes in regular expressions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text\n",
      " 'Ramanujan saw ௧௭௨௯ as 1729 = 1³ + 12³ = 9³ + 10³.'\n",
      "Numbers\n",
      " str : ['௧௭௨௯', '1729', '1', '12', '9', '10']\n",
      " bytes: [b'1729', b'1', b'12', b'9', b'10']\n",
      "Words\n",
      " str : ['Ramanujan', 'saw', '௧௭௨௯', 'as', '1729', '1³', '12³', '9³', '10³']\n",
      " bytes: [b'Ramanujan', b'saw', b'as', b'1729', b'1', b'12', b'9', b'10']\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "re_numbers_str = re.compile(r'\\d+')\n",
    "re_words_str = re.compile(r'\\w+')\n",
    "re_numbers_bytes = re.compile(rb'\\d+')\n",
    "re_words_bytes = re.compile(rb'\\w+')\n",
    "text_str = (\"Ramanujan saw \\u0be7\\u0bed\\u0be8\\u0bef\"\n",
    "\" as 1729 = 1³ + 12³ = 9³ + 10³.\")\n",
    "text_bytes = text_str.encode('utf_8')\n",
    "print('Text', repr(text_str), sep='\\n ')\n",
    "print('Numbers')\n",
    "print(' str :', re_numbers_str.findall(text_str))\n",
    "print(' bytes:', re_numbers_bytes.findall(text_bytes))\n",
    "print('Words')\n",
    "print(' str :', re_words_str.findall(text_str))\n",
    "print(' bytes:', re_words_bytes.findall(text_bytes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### str versus bytes on os functions\n",
    "To work around this issue, all os module functions that accept file names or path names\n",
    "take arguments as str or bytes. If one such function is called with a str argument, the\n",
    "argument will be automatically converted using the codec named by sys.getfilesys\n",
    "temencoding(), and the OS response will be decoded with the same codec. This is almost\n",
    "always what you want, in keeping with the Unicode sandwich best practice.\n",
    "\n",
    "But if you must deal with (and perhaps fix) file names that cannot be handled in that\n",
    "way, you can pass bytes arguments to the os functions to get bytes return values. This\n",
    "feature lets you deal with any file or path name, no matter how many gremlins you may\n",
    "find."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.git',\n",
       " '.ipynb_checkpoints',\n",
       " 'cafe.txt',\n",
       " 'defaultencodings.py',\n",
       " 'dummy',\n",
       " 'image001.png',\n",
       " 'PART I  Prologue.ipynb',\n",
       " 'Part II  Data structures.ipynb',\n",
       " 'README.md',\n",
       " 'zen.txt',\n",
       " 'zen.txt.bak',\n",
       " '新建文本文档.txt.bak']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'.git',\n",
       " b'.ipynb_checkpoints',\n",
       " b'cafe.txt',\n",
       " b'defaultencodings.py',\n",
       " b'dummy',\n",
       " b'image001.png',\n",
       " b'PART I  Prologue.ipynb',\n",
       " b'Part II  Data structures.ipynb',\n",
       " b'README.md',\n",
       " b'zen.txt',\n",
       " b'zen.txt.bak',\n",
       " b'\\xe6\\x96\\xb0\\xe5\\xbb\\xba\\xe6\\x96\\x87\\xe6\\x9c\\xac\\xe6\\x96\\x87\\xe6\\xa1\\xa3.txt.bak']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(b'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fsencode(filename)\n",
    "Encodes filename (can be str or bytes) to bytes using the codec named by\n",
    "sys.getfilesystemencoding() if filename is of type str, otherwise return the\n",
    "filename bytes unchanged.\n",
    "fsdecode(filename)\n",
    "Decodes filename (can be str or bytes) to str using the codec named by sys.get\n",
    "filesystemencoding() if filename is of type bytes, otherwise return the file\n",
    "name str unchanged."
   ]
  }
 ],
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